Unfolding Beijing in a hedonic way
Gespeichert in:
| Autor*in: |
Lin, Wei [verfasserIn] Shi, Zhentao [verfasserIn] Wang, Yishu [verfasserIn] Yan, Ting Hin [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Computational economics - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988, 61(2023), 1 vom: Jan., Seite 317-340 |
|---|---|
| Übergeordnetes Werk: |
volume:61 ; year:2023 ; number:1 ; month:01 ; pages:317-340 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10614-021-10209-3 |
|---|
| Katalog-ID: |
1838870040 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | 1838870040 | ||
| 003 | DE-627 | ||
| 005 | 20230710124530.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230310s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10614-021-10209-3 |2 doi | |
| 035 | |a (DE-627)1838870040 | ||
| 035 | |a (DE-599)KXP1838870040 | ||
| 035 | |a (DE-He213)s10614-021-10209-3-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 100 | 1 | |a Lin, Wei |e verfasserin |0 (DE-588)1284074129 |0 (DE-627)1839748508 |4 aut | |
| 245 | 1 | 0 | |a Unfolding Beijing in a hedonic way |c Wei Lin, Zhentao Shi, Yishu Wang, Ting Hin Yan |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 650 | 4 | |a Boosting |7 (dpeaa)DE-206 | |
| 650 | 4 | |a Housing price |7 (dpeaa)DE-206 | |
| 650 | 4 | |a Machine learning |7 (dpeaa)DE-206 | |
| 650 | 4 | |a Nonlinear estimation |7 (dpeaa)DE-206 | |
| 650 | 4 | |a Prediction |7 (dpeaa)DE-206 | |
| 650 | 4 | |a Regression trees |7 (dpeaa)DE-206 | |
| 700 | 1 | |a Shi, Zhentao |e verfasserin |0 (DE-588)138659818 |0 (DE-627)604644345 |0 (DE-576)308488008 |4 aut | |
| 700 | 1 | |a Wang, Yishu |e verfasserin |0 (DE-588)128407420X |0 (DE-627)1839748680 |4 aut | |
| 700 | 1 | |a Yan, Ting Hin |e verfasserin |0 (DE-588)1284074242 |0 (DE-627)1839748745 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Computational economics |d Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 |g 61(2023), 1 vom: Jan., Seite 317-340 |h Online-Ressource |w (DE-627)270427546 |w (DE-600)1477445-8 |w (DE-576)121190374 |x 1572-9974 |7 nnns |
| 773 | 1 | 8 | |g volume:61 |g year:2023 |g number:1 |g month:01 |g pages:317-340 |
| 856 | 4 | 0 | |u https://link.springer.com/content/pdf/10.1007/s10614-021-10209-3.pdf |x Verlag |z lizenzpflichtig |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10614-021-10209-3 |x Resolving-System |z lizenzpflichtig |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ILN_26 | ||
| 912 | |a ISIL_DE-206 | ||
| 912 | |a SYSFLAG_1 | ||
| 912 | |a GBV_KXP | ||
| 912 | |a GBV_ILN_11 | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_32 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_63 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_90 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_101 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_120 | ||
| 912 | |a GBV_ILN_138 | ||
| 912 | |a GBV_ILN_150 | ||
| 912 | |a GBV_ILN_151 | ||
| 912 | |a GBV_ILN_152 | ||
| 912 | |a GBV_ILN_161 | ||
| 912 | |a GBV_ILN_170 | ||
| 912 | |a GBV_ILN_171 | ||
| 912 | |a GBV_ILN_187 | ||
| 912 | |a GBV_ILN_213 | ||
| 912 | |a GBV_ILN_224 | ||
| 912 | |a GBV_ILN_230 | ||
| 912 | |a GBV_ILN_250 | ||
| 912 | |a GBV_ILN_281 | ||
| 912 | |a GBV_ILN_285 | ||
| 912 | |a GBV_ILN_293 | ||
| 912 | |a GBV_ILN_370 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_636 | ||
| 912 | |a GBV_ILN_702 | ||
| 912 | |a GBV_ILN_2001 | ||
| 912 | |a GBV_ILN_2003 | ||
| 912 | |a GBV_ILN_2004 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2007 | ||
| 912 | |a GBV_ILN_2008 | ||
| 912 | |a GBV_ILN_2009 | ||
| 912 | |a GBV_ILN_2010 | ||
| 912 | |a GBV_ILN_2011 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2015 | ||
| 912 | |a GBV_ILN_2020 | ||
| 912 | |a GBV_ILN_2021 | ||
| 912 | |a GBV_ILN_2025 | ||
| 912 | |a GBV_ILN_2026 | ||
| 912 | |a GBV_ILN_2027 | ||
| 912 | |a GBV_ILN_2031 | ||
| 912 | |a GBV_ILN_2034 | ||
| 912 | |a GBV_ILN_2037 | ||
| 912 | |a GBV_ILN_2038 | ||
| 912 | |a GBV_ILN_2039 | ||
| 912 | |a GBV_ILN_2044 | ||
| 912 | |a GBV_ILN_2048 | ||
| 912 | |a GBV_ILN_2049 | ||
| 912 | |a GBV_ILN_2050 | ||
| 912 | |a GBV_ILN_2055 | ||
| 912 | |a GBV_ILN_2056 | ||
| 912 | |a GBV_ILN_2057 | ||
| 912 | |a GBV_ILN_2059 | ||
| 912 | |a GBV_ILN_2061 | ||
| 912 | |a GBV_ILN_2064 | ||
| 912 | |a GBV_ILN_2065 | ||
| 912 | |a GBV_ILN_2068 | ||
| 912 | |a GBV_ILN_2088 | ||
| 912 | |a GBV_ILN_2093 | ||
| 912 | |a GBV_ILN_2106 | ||
| 912 | |a GBV_ILN_2107 | ||
| 912 | |a GBV_ILN_2108 | ||
| 912 | |a GBV_ILN_2110 | ||
| 912 | |a GBV_ILN_2111 | ||
| 912 | |a GBV_ILN_2112 | ||
| 912 | |a GBV_ILN_2113 | ||
| 912 | |a GBV_ILN_2118 | ||
| 912 | |a GBV_ILN_2122 | ||
| 912 | |a GBV_ILN_2129 | ||
| 912 | |a GBV_ILN_2143 | ||
| 912 | |a GBV_ILN_2144 | ||
| 912 | |a GBV_ILN_2147 | ||
| 912 | |a GBV_ILN_2148 | ||
| 912 | |a GBV_ILN_2152 | ||
| 912 | |a GBV_ILN_2153 | ||
| 912 | |a GBV_ILN_2188 | ||
| 912 | |a GBV_ILN_2190 | ||
| 912 | |a GBV_ILN_2232 | ||
| 912 | |a GBV_ILN_2336 | ||
| 912 | |a GBV_ILN_2446 | ||
| 912 | |a GBV_ILN_2470 | ||
| 912 | |a GBV_ILN_2472 | ||
| 912 | |a GBV_ILN_2507 | ||
| 912 | |a GBV_ILN_2522 | ||
| 912 | |a GBV_ILN_2548 | ||
| 912 | |a GBV_ILN_4035 | ||
| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4112 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4242 | ||
| 912 | |a GBV_ILN_4246 | ||
| 912 | |a GBV_ILN_4249 | ||
| 912 | |a GBV_ILN_4251 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4307 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4322 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4325 | ||
| 912 | |a GBV_ILN_4326 | ||
| 912 | |a GBV_ILN_4328 | ||
| 912 | |a GBV_ILN_4333 | ||
| 912 | |a GBV_ILN_4334 | ||
| 912 | |a GBV_ILN_4335 | ||
| 912 | |a GBV_ILN_4336 | ||
| 912 | |a GBV_ILN_4338 | ||
| 912 | |a GBV_ILN_4393 | ||
| 912 | |a GBV_ILN_4700 | ||
| 951 | |a AR | ||
| 952 | |d 61 |j 2023 |e 1 |c 1 |h 317-340 | ||
| 980 | |2 26 |1 01 |x 0206 |b 4287005430 |y x1z |z 10-03-23 | ||
| 982 | |2 26 |1 00 |x DE-206 |b The housing market is of tremendous importance to the Chinese economy. Housing prices in a metropolitan like Beijing are determined not only by the structural attributes of housing units, but also by the externality stemming from local amenities and their investment potential. Meanwhile, the traditional hedonic pricing model fails to capture the latter two aspects due to its inability to capture the spatial and temporal dimensions of the housing market. In this paper, we augment the traditional model by introducing machine learning algorithms that can handle the additional complexity arising from time and space. Using a transaction-level dataset of housing prices in Beijing, we compare the performance of random forest and Gradient Boosting Machine (GBM) with Ordinary Least Squares (OLS), k-Nearest Neighbor (KNN) and local polynomial. We find that GBM significantly outperforms the other methods in spatial prediction and sequential forecast. GBM's superior predictive capacity indicates the potential of machine learning techniques in reducing the costs of real estate appraisal, alleviating information asymmetry in the housing market, and improving people's welfare given the implications of home ownership on economic inequality and social mobility. | ||
| author_variant |
w l wl z s zs y w yw t h y th thy |
|---|---|
| matchkey_str |
article:15729974:2023----::nodnbiignh |
| hierarchy_sort_str |
2023 |
| publishDate |
2023 |
| allfields |
10.1007/s10614-021-10209-3 doi (DE-627)1838870040 (DE-599)KXP1838870040 (DE-He213)s10614-021-10209-3-e DE-627 ger DE-627 rda eng Lin, Wei verfasserin (DE-588)1284074129 (DE-627)1839748508 aut Unfolding Beijing in a hedonic way Wei Lin, Zhentao Shi, Yishu Wang, Ting Hin Yan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Boosting (dpeaa)DE-206 Housing price (dpeaa)DE-206 Machine learning (dpeaa)DE-206 Nonlinear estimation (dpeaa)DE-206 Prediction (dpeaa)DE-206 Regression trees (dpeaa)DE-206 Shi, Zhentao verfasserin (DE-588)138659818 (DE-627)604644345 (DE-576)308488008 aut Wang, Yishu verfasserin (DE-588)128407420X (DE-627)1839748680 aut Yan, Ting Hin verfasserin (DE-588)1284074242 (DE-627)1839748745 aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 61(2023), 1 vom: Jan., Seite 317-340 Online-Ressource (DE-627)270427546 (DE-600)1477445-8 (DE-576)121190374 1572-9974 nnns volume:61 year:2023 number:1 month:01 pages:317-340 https://link.springer.com/content/pdf/10.1007/s10614-021-10209-3.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10614-021-10209-3 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2023 1 1 317-340 26 01 0206 4287005430 x1z 10-03-23 26 00 DE-206 The housing market is of tremendous importance to the Chinese economy. Housing prices in a metropolitan like Beijing are determined not only by the structural attributes of housing units, but also by the externality stemming from local amenities and their investment potential. Meanwhile, the traditional hedonic pricing model fails to capture the latter two aspects due to its inability to capture the spatial and temporal dimensions of the housing market. In this paper, we augment the traditional model by introducing machine learning algorithms that can handle the additional complexity arising from time and space. Using a transaction-level dataset of housing prices in Beijing, we compare the performance of random forest and Gradient Boosting Machine (GBM) with Ordinary Least Squares (OLS), k-Nearest Neighbor (KNN) and local polynomial. We find that GBM significantly outperforms the other methods in spatial prediction and sequential forecast. GBM's superior predictive capacity indicates the potential of machine learning techniques in reducing the costs of real estate appraisal, alleviating information asymmetry in the housing market, and improving people's welfare given the implications of home ownership on economic inequality and social mobility. |
| spelling |
10.1007/s10614-021-10209-3 doi (DE-627)1838870040 (DE-599)KXP1838870040 (DE-He213)s10614-021-10209-3-e DE-627 ger DE-627 rda eng Lin, Wei verfasserin (DE-588)1284074129 (DE-627)1839748508 aut Unfolding Beijing in a hedonic way Wei Lin, Zhentao Shi, Yishu Wang, Ting Hin Yan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Boosting (dpeaa)DE-206 Housing price (dpeaa)DE-206 Machine learning (dpeaa)DE-206 Nonlinear estimation (dpeaa)DE-206 Prediction (dpeaa)DE-206 Regression trees (dpeaa)DE-206 Shi, Zhentao verfasserin (DE-588)138659818 (DE-627)604644345 (DE-576)308488008 aut Wang, Yishu verfasserin (DE-588)128407420X (DE-627)1839748680 aut Yan, Ting Hin verfasserin (DE-588)1284074242 (DE-627)1839748745 aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 61(2023), 1 vom: Jan., Seite 317-340 Online-Ressource (DE-627)270427546 (DE-600)1477445-8 (DE-576)121190374 1572-9974 nnns volume:61 year:2023 number:1 month:01 pages:317-340 https://link.springer.com/content/pdf/10.1007/s10614-021-10209-3.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10614-021-10209-3 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2023 1 1 317-340 26 01 0206 4287005430 x1z 10-03-23 26 00 DE-206 The housing market is of tremendous importance to the Chinese economy. Housing prices in a metropolitan like Beijing are determined not only by the structural attributes of housing units, but also by the externality stemming from local amenities and their investment potential. Meanwhile, the traditional hedonic pricing model fails to capture the latter two aspects due to its inability to capture the spatial and temporal dimensions of the housing market. In this paper, we augment the traditional model by introducing machine learning algorithms that can handle the additional complexity arising from time and space. Using a transaction-level dataset of housing prices in Beijing, we compare the performance of random forest and Gradient Boosting Machine (GBM) with Ordinary Least Squares (OLS), k-Nearest Neighbor (KNN) and local polynomial. We find that GBM significantly outperforms the other methods in spatial prediction and sequential forecast. GBM's superior predictive capacity indicates the potential of machine learning techniques in reducing the costs of real estate appraisal, alleviating information asymmetry in the housing market, and improving people's welfare given the implications of home ownership on economic inequality and social mobility. |
| allfields_unstemmed |
10.1007/s10614-021-10209-3 doi (DE-627)1838870040 (DE-599)KXP1838870040 (DE-He213)s10614-021-10209-3-e DE-627 ger DE-627 rda eng Lin, Wei verfasserin (DE-588)1284074129 (DE-627)1839748508 aut Unfolding Beijing in a hedonic way Wei Lin, Zhentao Shi, Yishu Wang, Ting Hin Yan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Boosting (dpeaa)DE-206 Housing price (dpeaa)DE-206 Machine learning (dpeaa)DE-206 Nonlinear estimation (dpeaa)DE-206 Prediction (dpeaa)DE-206 Regression trees (dpeaa)DE-206 Shi, Zhentao verfasserin (DE-588)138659818 (DE-627)604644345 (DE-576)308488008 aut Wang, Yishu verfasserin (DE-588)128407420X (DE-627)1839748680 aut Yan, Ting Hin verfasserin (DE-588)1284074242 (DE-627)1839748745 aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 61(2023), 1 vom: Jan., Seite 317-340 Online-Ressource (DE-627)270427546 (DE-600)1477445-8 (DE-576)121190374 1572-9974 nnns volume:61 year:2023 number:1 month:01 pages:317-340 https://link.springer.com/content/pdf/10.1007/s10614-021-10209-3.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10614-021-10209-3 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2023 1 1 317-340 26 01 0206 4287005430 x1z 10-03-23 26 00 DE-206 The housing market is of tremendous importance to the Chinese economy. Housing prices in a metropolitan like Beijing are determined not only by the structural attributes of housing units, but also by the externality stemming from local amenities and their investment potential. Meanwhile, the traditional hedonic pricing model fails to capture the latter two aspects due to its inability to capture the spatial and temporal dimensions of the housing market. In this paper, we augment the traditional model by introducing machine learning algorithms that can handle the additional complexity arising from time and space. Using a transaction-level dataset of housing prices in Beijing, we compare the performance of random forest and Gradient Boosting Machine (GBM) with Ordinary Least Squares (OLS), k-Nearest Neighbor (KNN) and local polynomial. We find that GBM significantly outperforms the other methods in spatial prediction and sequential forecast. GBM's superior predictive capacity indicates the potential of machine learning techniques in reducing the costs of real estate appraisal, alleviating information asymmetry in the housing market, and improving people's welfare given the implications of home ownership on economic inequality and social mobility. |
| allfieldsGer |
10.1007/s10614-021-10209-3 doi (DE-627)1838870040 (DE-599)KXP1838870040 (DE-He213)s10614-021-10209-3-e DE-627 ger DE-627 rda eng Lin, Wei verfasserin (DE-588)1284074129 (DE-627)1839748508 aut Unfolding Beijing in a hedonic way Wei Lin, Zhentao Shi, Yishu Wang, Ting Hin Yan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Boosting (dpeaa)DE-206 Housing price (dpeaa)DE-206 Machine learning (dpeaa)DE-206 Nonlinear estimation (dpeaa)DE-206 Prediction (dpeaa)DE-206 Regression trees (dpeaa)DE-206 Shi, Zhentao verfasserin (DE-588)138659818 (DE-627)604644345 (DE-576)308488008 aut Wang, Yishu verfasserin (DE-588)128407420X (DE-627)1839748680 aut Yan, Ting Hin verfasserin (DE-588)1284074242 (DE-627)1839748745 aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 61(2023), 1 vom: Jan., Seite 317-340 Online-Ressource (DE-627)270427546 (DE-600)1477445-8 (DE-576)121190374 1572-9974 nnns volume:61 year:2023 number:1 month:01 pages:317-340 https://link.springer.com/content/pdf/10.1007/s10614-021-10209-3.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10614-021-10209-3 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2023 1 1 317-340 26 01 0206 4287005430 x1z 10-03-23 26 00 DE-206 The housing market is of tremendous importance to the Chinese economy. Housing prices in a metropolitan like Beijing are determined not only by the structural attributes of housing units, but also by the externality stemming from local amenities and their investment potential. Meanwhile, the traditional hedonic pricing model fails to capture the latter two aspects due to its inability to capture the spatial and temporal dimensions of the housing market. In this paper, we augment the traditional model by introducing machine learning algorithms that can handle the additional complexity arising from time and space. Using a transaction-level dataset of housing prices in Beijing, we compare the performance of random forest and Gradient Boosting Machine (GBM) with Ordinary Least Squares (OLS), k-Nearest Neighbor (KNN) and local polynomial. We find that GBM significantly outperforms the other methods in spatial prediction and sequential forecast. GBM's superior predictive capacity indicates the potential of machine learning techniques in reducing the costs of real estate appraisal, alleviating information asymmetry in the housing market, and improving people's welfare given the implications of home ownership on economic inequality and social mobility. |
| allfieldsSound |
10.1007/s10614-021-10209-3 doi (DE-627)1838870040 (DE-599)KXP1838870040 (DE-He213)s10614-021-10209-3-e DE-627 ger DE-627 rda eng Lin, Wei verfasserin (DE-588)1284074129 (DE-627)1839748508 aut Unfolding Beijing in a hedonic way Wei Lin, Zhentao Shi, Yishu Wang, Ting Hin Yan 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Boosting (dpeaa)DE-206 Housing price (dpeaa)DE-206 Machine learning (dpeaa)DE-206 Nonlinear estimation (dpeaa)DE-206 Prediction (dpeaa)DE-206 Regression trees (dpeaa)DE-206 Shi, Zhentao verfasserin (DE-588)138659818 (DE-627)604644345 (DE-576)308488008 aut Wang, Yishu verfasserin (DE-588)128407420X (DE-627)1839748680 aut Yan, Ting Hin verfasserin (DE-588)1284074242 (DE-627)1839748745 aut Enthalten in Computational economics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988 61(2023), 1 vom: Jan., Seite 317-340 Online-Ressource (DE-627)270427546 (DE-600)1477445-8 (DE-576)121190374 1572-9974 nnns volume:61 year:2023 number:1 month:01 pages:317-340 https://link.springer.com/content/pdf/10.1007/s10614-021-10209-3.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10614-021-10209-3 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2023 1 1 317-340 26 01 0206 4287005430 x1z 10-03-23 26 00 DE-206 The housing market is of tremendous importance to the Chinese economy. Housing prices in a metropolitan like Beijing are determined not only by the structural attributes of housing units, but also by the externality stemming from local amenities and their investment potential. Meanwhile, the traditional hedonic pricing model fails to capture the latter two aspects due to its inability to capture the spatial and temporal dimensions of the housing market. In this paper, we augment the traditional model by introducing machine learning algorithms that can handle the additional complexity arising from time and space. Using a transaction-level dataset of housing prices in Beijing, we compare the performance of random forest and Gradient Boosting Machine (GBM) with Ordinary Least Squares (OLS), k-Nearest Neighbor (KNN) and local polynomial. We find that GBM significantly outperforms the other methods in spatial prediction and sequential forecast. GBM's superior predictive capacity indicates the potential of machine learning techniques in reducing the costs of real estate appraisal, alleviating information asymmetry in the housing market, and improving people's welfare given the implications of home ownership on economic inequality and social mobility. |
| language |
English |
| source |
Enthalten in Computational economics 61(2023), 1 vom: Jan., Seite 317-340 volume:61 year:2023 number:1 month:01 pages:317-340 |
| sourceStr |
Enthalten in Computational economics 61(2023), 1 vom: Jan., Seite 317-340 volume:61 year:2023 number:1 month:01 pages:317-340 |
| format_phy_str_mv |
Article |
| building |
26:1 |
| institution |
findex.gbv.de |
| selectbib_iln_str_mv |
26@1z |
| topic_facet |
Boosting Housing price Machine learning Nonlinear estimation Prediction Regression trees |
| sw_local_iln_str_mv |
26: DE-206: |
| isfreeaccess_bool |
false |
| container_title |
Computational economics |
| authorswithroles_txt_mv |
Lin, Wei @@aut@@ Shi, Zhentao @@aut@@ Wang, Yishu @@aut@@ Yan, Ting Hin @@aut@@ |
| publishDateDaySort_date |
2023-01-01T00:00:00Z |
| hierarchy_top_id |
270427546 |
| id |
1838870040 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1838870040</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230710124530.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230310s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10614-021-10209-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1838870040</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1838870040</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10614-021-10209-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lin, Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1284074129</subfield><subfield code="0">(DE-627)1839748508</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Unfolding Beijing in a hedonic way</subfield><subfield code="c">Wei Lin, Zhentao Shi, Yishu Wang, Ting Hin Yan</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Boosting</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Housing price</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonlinear estimation</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prediction</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression trees</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shi, Zhentao</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)138659818</subfield><subfield code="0">(DE-627)604644345</subfield><subfield code="0">(DE-576)308488008</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Yishu</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)128407420X</subfield><subfield code="0">(DE-627)1839748680</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yan, Ting Hin</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1284074242</subfield><subfield code="0">(DE-627)1839748745</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computational economics</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988</subfield><subfield code="g">61(2023), 1 vom: Jan., Seite 317-340</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)270427546</subfield><subfield code="w">(DE-600)1477445-8</subfield><subfield code="w">(DE-576)121190374</subfield><subfield code="x">1572-9974</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:61</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:317-340</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://link.springer.com/content/pdf/10.1007/s10614-021-10209-3.pdf</subfield><subfield code="x">Verlag</subfield><subfield code="z">lizenzpflichtig</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/s10614-021-10209-3</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">lizenzpflichtig</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2188</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2446</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">61</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="c">1</subfield><subfield code="h">317-340</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4287005430</subfield><subfield code="y">x1z</subfield><subfield code="z">10-03-23</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">The housing market is of tremendous importance to the Chinese economy. Housing prices in a metropolitan like Beijing are determined not only by the structural attributes of housing units, but also by the externality stemming from local amenities and their investment potential. Meanwhile, the traditional hedonic pricing model fails to capture the latter two aspects due to its inability to capture the spatial and temporal dimensions of the housing market. In this paper, we augment the traditional model by introducing machine learning algorithms that can handle the additional complexity arising from time and space. Using a transaction-level dataset of housing prices in Beijing, we compare the performance of random forest and Gradient Boosting Machine (GBM) with Ordinary Least Squares (OLS), k-Nearest Neighbor (KNN) and local polynomial. We find that GBM significantly outperforms the other methods in spatial prediction and sequential forecast. GBM's superior predictive capacity indicates the potential of machine learning techniques in reducing the costs of real estate appraisal, alleviating information asymmetry in the housing market, and improving people's welfare given the implications of home ownership on economic inequality and social mobility.</subfield></datafield></record></collection>
|
| author |
Lin, Wei |
| spellingShingle |
Lin, Wei misc Boosting misc Housing price misc Machine learning misc Nonlinear estimation misc Prediction misc Regression trees Unfolding Beijing in a hedonic way |
| authorStr |
Lin, Wei |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)270427546 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| typewithnormlink_str_mv |
Person@(DE-588)1284074129 DifferentiatedPerson@(DE-588)1284074129 Person@(DE-588)138659818 DifferentiatedPerson@(DE-588)138659818 Person@(DE-588)128407420X DifferentiatedPerson@(DE-588)128407420X Person@(DE-588)1284074242 DifferentiatedPerson@(DE-588)1284074242 |
| collection |
KXP GVK SWB |
| remote_str |
true |
| last_changed_iln_str_mv |
26@10-03-23 |
| illustrated |
Not Illustrated |
| issn |
1572-9974 |
| topic_title |
26 00 DE-206 The housing market is of tremendous importance to the Chinese economy. Housing prices in a metropolitan like Beijing are determined not only by the structural attributes of housing units, but also by the externality stemming from local amenities and their investment potential. Meanwhile, the traditional hedonic pricing model fails to capture the latter two aspects due to its inability to capture the spatial and temporal dimensions of the housing market. In this paper, we augment the traditional model by introducing machine learning algorithms that can handle the additional complexity arising from time and space. Using a transaction-level dataset of housing prices in Beijing, we compare the performance of random forest and Gradient Boosting Machine (GBM) with Ordinary Least Squares (OLS), k-Nearest Neighbor (KNN) and local polynomial. We find that GBM significantly outperforms the other methods in spatial prediction and sequential forecast. GBM's superior predictive capacity indicates the potential of machine learning techniques in reducing the costs of real estate appraisal, alleviating information asymmetry in the housing market, and improving people's welfare given the implications of home ownership on economic inequality and social mobility Unfolding Beijing in a hedonic way Wei Lin, Zhentao Shi, Yishu Wang, Ting Hin Yan Boosting (dpeaa)DE-206 Housing price (dpeaa)DE-206 Machine learning (dpeaa)DE-206 Nonlinear estimation (dpeaa)DE-206 Prediction (dpeaa)DE-206 Regression trees (dpeaa)DE-206 |
| topic |
misc Boosting misc Housing price misc Machine learning misc Nonlinear estimation misc Prediction misc Regression trees |
| topic_unstemmed |
misc Boosting misc Housing price misc Machine learning misc Nonlinear estimation misc Prediction misc Regression trees |
| topic_browse |
misc Boosting misc Housing price misc Machine learning misc Nonlinear estimation misc Prediction misc Regression trees |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Computational economics |
| normlinkwithtype_str_mv |
(DE-588)1284074129@Person (DE-588)1284074129@DifferentiatedPerson (DE-588)138659818@Person (DE-588)138659818@DifferentiatedPerson (DE-588)128407420X@Person (DE-588)128407420X@DifferentiatedPerson (DE-588)1284074242@Person (DE-588)1284074242@DifferentiatedPerson |
| hierarchy_parent_id |
270427546 |
| hierarchy_top_title |
Computational economics |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)270427546 (DE-600)1477445-8 (DE-576)121190374 |
| normlinkwithrole_str_mv |
(DE-588)1284074129@@aut@@ (DE-588)138659818@@aut@@ (DE-588)128407420X@@aut@@ (DE-588)1284074242@@aut@@ |
| title |
Unfolding Beijing in a hedonic way |
| ctrlnum |
(DE-627)1838870040 (DE-599)KXP1838870040 (DE-He213)s10614-021-10209-3-e |
| title_full |
Unfolding Beijing in a hedonic way Wei Lin, Zhentao Shi, Yishu Wang, Ting Hin Yan |
| author_sort |
Lin, Wei |
| journal |
Computational economics |
| journalStr |
Computational economics |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2023 |
| contenttype_str_mv |
txt |
| container_start_page |
317 |
| author_browse |
Lin, Wei Shi, Zhentao Wang, Yishu Yan, Ting Hin |
| selectkey |
26:x |
| container_volume |
61 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Lin, Wei |
| doi_str_mv |
10.1007/s10614-021-10209-3 |
| normlink |
1284074129 1839748508 138659818 604644345 308488008 128407420X 1839748680 1284074242 1839748745 |
| normlink_prefix_str_mv |
(DE-588)1284074129 (DE-627)1839748508 (DE-588)138659818 (DE-627)604644345 (DE-576)308488008 (DE-588)128407420X (DE-627)1839748680 (DE-588)1284074242 (DE-627)1839748745 |
| author2-role |
verfasserin |
| title_sort |
unfolding beijing in a hedonic way |
| title_auth |
Unfolding Beijing in a hedonic way |
| collection_details |
GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 |
| container_issue |
1 |
| title_short |
Unfolding Beijing in a hedonic way |
| url |
https://link.springer.com/content/pdf/10.1007/s10614-021-10209-3.pdf https://doi.org/10.1007/s10614-021-10209-3 |
| ausleihindikator_str_mv |
26 |
| rolewithnormlink_str_mv |
@@aut@@(DE-588)1284074129 @@aut@@(DE-588)138659818 @@aut@@(DE-588)128407420X @@aut@@(DE-588)1284074242 |
| remote_bool |
true |
| author2 |
Shi, Zhentao Wang, Yishu Yan, Ting Hin |
| author2Str |
Shi, Zhentao Wang, Yishu Yan, Ting Hin |
| ppnlink |
270427546 |
| GND_str_mv |
Wei, Lin Lin, W. Lin Wei Lin, Wei Zhentao, Shi Shi Zhentao Shi, Z. Shi, Zhentao Yishu, Wang Wang, Y. Wang Yishu Wang, Yishu Yan Ting Hin Ting, Hin Yan Yan, T. H. Hin Yan, Ting Yan, Ting Hin |
| GND_txt_mv |
Wei, Lin Lin, W. Lin Wei Lin, Wei Zhentao, Shi Shi Zhentao Shi, Z. Shi, Zhentao Yishu, Wang Wang, Y. Wang Yishu Wang, Yishu Yan Ting Hin Ting, Hin Yan Yan, T. H. Hin Yan, Ting Yan, Ting Hin |
| GND_txtF_mv |
Wei, Lin Lin, W. Lin Wei Lin, Wei Zhentao, Shi Shi Zhentao Shi, Z. Shi, Zhentao Yishu, Wang Wang, Y. Wang Yishu Wang, Yishu Yan Ting Hin Ting, Hin Yan Yan, T. H. Hin Yan, Ting Yan, Ting Hin |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10614-021-10209-3 |
| up_date |
2024-07-04T10:18:19.123Z |
| _version_ |
1803643308344344576 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1838870040</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230710124530.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230310s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10614-021-10209-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1838870040</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1838870040</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10614-021-10209-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lin, Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1284074129</subfield><subfield code="0">(DE-627)1839748508</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Unfolding Beijing in a hedonic way</subfield><subfield code="c">Wei Lin, Zhentao Shi, Yishu Wang, Ting Hin Yan</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Boosting</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Housing price</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonlinear estimation</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prediction</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression trees</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shi, Zhentao</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)138659818</subfield><subfield code="0">(DE-627)604644345</subfield><subfield code="0">(DE-576)308488008</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Yishu</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)128407420X</subfield><subfield code="0">(DE-627)1839748680</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yan, Ting Hin</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1284074242</subfield><subfield code="0">(DE-627)1839748745</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computational economics</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V., 1988</subfield><subfield code="g">61(2023), 1 vom: Jan., Seite 317-340</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)270427546</subfield><subfield code="w">(DE-600)1477445-8</subfield><subfield code="w">(DE-576)121190374</subfield><subfield code="x">1572-9974</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:61</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:317-340</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://link.springer.com/content/pdf/10.1007/s10614-021-10209-3.pdf</subfield><subfield code="x">Verlag</subfield><subfield code="z">lizenzpflichtig</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/s10614-021-10209-3</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">lizenzpflichtig</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2188</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2446</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">61</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="c">1</subfield><subfield code="h">317-340</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4287005430</subfield><subfield code="y">x1z</subfield><subfield code="z">10-03-23</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">The housing market is of tremendous importance to the Chinese economy. Housing prices in a metropolitan like Beijing are determined not only by the structural attributes of housing units, but also by the externality stemming from local amenities and their investment potential. Meanwhile, the traditional hedonic pricing model fails to capture the latter two aspects due to its inability to capture the spatial and temporal dimensions of the housing market. In this paper, we augment the traditional model by introducing machine learning algorithms that can handle the additional complexity arising from time and space. Using a transaction-level dataset of housing prices in Beijing, we compare the performance of random forest and Gradient Boosting Machine (GBM) with Ordinary Least Squares (OLS), k-Nearest Neighbor (KNN) and local polynomial. We find that GBM significantly outperforms the other methods in spatial prediction and sequential forecast. GBM's superior predictive capacity indicates the potential of machine learning techniques in reducing the costs of real estate appraisal, alleviating information asymmetry in the housing market, and improving people's welfare given the implications of home ownership on economic inequality and social mobility.</subfield></datafield></record></collection>
|
| score |
7.3975754 |