Big Data in Real Estate? From Manual Appraisal to Automated Valuation
Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. In this article, the authors provide a practical application of big data by employing a unique set of data on U.S. multifamily assets, in combi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kok, Nils [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: The journal of portfolio management - New York, NY : Pageant Media Ltd., 1974, 43(2017), 6, Seite 202 |
|---|---|
| Übergeordnetes Werk: |
volume:43 ; year:2017 ; number:6 ; pages:202 |
| Links: |
|---|
| DOI / URN: |
10.3905/jpm.2017.43.6.202 |
|---|
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OLC1997194902 |
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| 520 | |a Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. In this article, the authors provide a practical application of big data by employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. The authors find strong evidence of the superiority of automated valuation models over traditional appraisals: The absolute error of the automated model is 9%, which compares favorably against the accuracy of traditional appraisals, and the model can produce an instant value at every moment in time at a very low cost. The authors also provide evidence of the importance of using hyperlocal information on the location of an asset. The model developed in this article is directly applicable for real estate lenders and investors and has important implications for the traditional appraisal industry. | ||
| 650 | 4 | |a Commercial real estate | |
| 650 | 4 | |a Economic aspects | |
| 650 | 4 | |a Market capitalization | |
| 650 | 4 | |a Analysis | |
| 650 | 4 | |a Valuation | |
| 650 | 4 | |a Real property | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Asset allocation | |
| 650 | 4 | |a Product differentiation | |
| 650 | 4 | |a Automation | |
| 650 | 4 | |a Studies | |
| 650 | 4 | |a Research | |
| 650 | 4 | |a Real estate appraisal | |
| 650 | 4 | |a Real estate financing | |
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10.3905/jpm.2017.43.6.202 doi PQ20171125 (DE-627)OLC1997194902 (DE-599)GBVOLC1997194902 (PRQ)g510-ee2d9f427a9fae38f00bbb849c0a2c7eb0eb64dce373678496dd8d8eedeaa3de0 (KEY)0045929120170000043000600202bigdatainrealestatefrommanualappraisaltoautomatedv DE-627 ger DE-627 rakwb eng 330 DNB 83.00 bkl Kok, Nils verfasserin aut Big Data in Real Estate? From Manual Appraisal to Automated Valuation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. In this article, the authors provide a practical application of big data by employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. The authors find strong evidence of the superiority of automated valuation models over traditional appraisals: The absolute error of the automated model is 9%, which compares favorably against the accuracy of traditional appraisals, and the model can produce an instant value at every moment in time at a very low cost. The authors also provide evidence of the importance of using hyperlocal information on the location of an asset. The model developed in this article is directly applicable for real estate lenders and investors and has important implications for the traditional appraisal industry. Commercial real estate Economic aspects Market capitalization Analysis Valuation Real property Artificial intelligence Asset allocation Product differentiation Automation Studies Research Real estate appraisal Real estate financing Koponen, Eija-Leena oth Martinez-Barbosa, Carmen Adriana oth Enthalten in The journal of portfolio management New York, NY : Pageant Media Ltd., 1974 43(2017), 6, Seite 202 (DE-627)129449377 (DE-600)197145-1 (DE-576)014815338 0095-4918 nnns volume:43 year:2017 number:6 pages:202 http://dx.doi.org/10.3905/jpm.2017.43.6.202 Volltext https://search.proquest.com/docview/1943019770 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_21 GBV_ILN_26 GBV_ILN_62 GBV_ILN_65 GBV_ILN_285 GBV_ILN_2009 GBV_ILN_2026 GBV_ILN_4012 GBV_ILN_4125 GBV_ILN_4310 GBV_ILN_4322 83.00 AVZ AR 43 2017 6 202 |
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10.3905/jpm.2017.43.6.202 doi PQ20171125 (DE-627)OLC1997194902 (DE-599)GBVOLC1997194902 (PRQ)g510-ee2d9f427a9fae38f00bbb849c0a2c7eb0eb64dce373678496dd8d8eedeaa3de0 (KEY)0045929120170000043000600202bigdatainrealestatefrommanualappraisaltoautomatedv DE-627 ger DE-627 rakwb eng 330 DNB 83.00 bkl Kok, Nils verfasserin aut Big Data in Real Estate? From Manual Appraisal to Automated Valuation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. In this article, the authors provide a practical application of big data by employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. The authors find strong evidence of the superiority of automated valuation models over traditional appraisals: The absolute error of the automated model is 9%, which compares favorably against the accuracy of traditional appraisals, and the model can produce an instant value at every moment in time at a very low cost. The authors also provide evidence of the importance of using hyperlocal information on the location of an asset. The model developed in this article is directly applicable for real estate lenders and investors and has important implications for the traditional appraisal industry. Commercial real estate Economic aspects Market capitalization Analysis Valuation Real property Artificial intelligence Asset allocation Product differentiation Automation Studies Research Real estate appraisal Real estate financing Koponen, Eija-Leena oth Martinez-Barbosa, Carmen Adriana oth Enthalten in The journal of portfolio management New York, NY : Pageant Media Ltd., 1974 43(2017), 6, Seite 202 (DE-627)129449377 (DE-600)197145-1 (DE-576)014815338 0095-4918 nnns volume:43 year:2017 number:6 pages:202 http://dx.doi.org/10.3905/jpm.2017.43.6.202 Volltext https://search.proquest.com/docview/1943019770 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_21 GBV_ILN_26 GBV_ILN_62 GBV_ILN_65 GBV_ILN_285 GBV_ILN_2009 GBV_ILN_2026 GBV_ILN_4012 GBV_ILN_4125 GBV_ILN_4310 GBV_ILN_4322 83.00 AVZ AR 43 2017 6 202 |
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10.3905/jpm.2017.43.6.202 doi PQ20171125 (DE-627)OLC1997194902 (DE-599)GBVOLC1997194902 (PRQ)g510-ee2d9f427a9fae38f00bbb849c0a2c7eb0eb64dce373678496dd8d8eedeaa3de0 (KEY)0045929120170000043000600202bigdatainrealestatefrommanualappraisaltoautomatedv DE-627 ger DE-627 rakwb eng 330 DNB 83.00 bkl Kok, Nils verfasserin aut Big Data in Real Estate? From Manual Appraisal to Automated Valuation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. In this article, the authors provide a practical application of big data by employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. The authors find strong evidence of the superiority of automated valuation models over traditional appraisals: The absolute error of the automated model is 9%, which compares favorably against the accuracy of traditional appraisals, and the model can produce an instant value at every moment in time at a very low cost. The authors also provide evidence of the importance of using hyperlocal information on the location of an asset. The model developed in this article is directly applicable for real estate lenders and investors and has important implications for the traditional appraisal industry. Commercial real estate Economic aspects Market capitalization Analysis Valuation Real property Artificial intelligence Asset allocation Product differentiation Automation Studies Research Real estate appraisal Real estate financing Koponen, Eija-Leena oth Martinez-Barbosa, Carmen Adriana oth Enthalten in The journal of portfolio management New York, NY : Pageant Media Ltd., 1974 43(2017), 6, Seite 202 (DE-627)129449377 (DE-600)197145-1 (DE-576)014815338 0095-4918 nnns volume:43 year:2017 number:6 pages:202 http://dx.doi.org/10.3905/jpm.2017.43.6.202 Volltext https://search.proquest.com/docview/1943019770 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_21 GBV_ILN_26 GBV_ILN_62 GBV_ILN_65 GBV_ILN_285 GBV_ILN_2009 GBV_ILN_2026 GBV_ILN_4012 GBV_ILN_4125 GBV_ILN_4310 GBV_ILN_4322 83.00 AVZ AR 43 2017 6 202 |
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10.3905/jpm.2017.43.6.202 doi PQ20171125 (DE-627)OLC1997194902 (DE-599)GBVOLC1997194902 (PRQ)g510-ee2d9f427a9fae38f00bbb849c0a2c7eb0eb64dce373678496dd8d8eedeaa3de0 (KEY)0045929120170000043000600202bigdatainrealestatefrommanualappraisaltoautomatedv DE-627 ger DE-627 rakwb eng 330 DNB 83.00 bkl Kok, Nils verfasserin aut Big Data in Real Estate? From Manual Appraisal to Automated Valuation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. In this article, the authors provide a practical application of big data by employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. The authors find strong evidence of the superiority of automated valuation models over traditional appraisals: The absolute error of the automated model is 9%, which compares favorably against the accuracy of traditional appraisals, and the model can produce an instant value at every moment in time at a very low cost. The authors also provide evidence of the importance of using hyperlocal information on the location of an asset. The model developed in this article is directly applicable for real estate lenders and investors and has important implications for the traditional appraisal industry. Commercial real estate Economic aspects Market capitalization Analysis Valuation Real property Artificial intelligence Asset allocation Product differentiation Automation Studies Research Real estate appraisal Real estate financing Koponen, Eija-Leena oth Martinez-Barbosa, Carmen Adriana oth Enthalten in The journal of portfolio management New York, NY : Pageant Media Ltd., 1974 43(2017), 6, Seite 202 (DE-627)129449377 (DE-600)197145-1 (DE-576)014815338 0095-4918 nnns volume:43 year:2017 number:6 pages:202 http://dx.doi.org/10.3905/jpm.2017.43.6.202 Volltext https://search.proquest.com/docview/1943019770 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_21 GBV_ILN_26 GBV_ILN_62 GBV_ILN_65 GBV_ILN_285 GBV_ILN_2009 GBV_ILN_2026 GBV_ILN_4012 GBV_ILN_4125 GBV_ILN_4310 GBV_ILN_4322 83.00 AVZ AR 43 2017 6 202 |
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10.3905/jpm.2017.43.6.202 doi PQ20171125 (DE-627)OLC1997194902 (DE-599)GBVOLC1997194902 (PRQ)g510-ee2d9f427a9fae38f00bbb849c0a2c7eb0eb64dce373678496dd8d8eedeaa3de0 (KEY)0045929120170000043000600202bigdatainrealestatefrommanualappraisaltoautomatedv DE-627 ger DE-627 rakwb eng 330 DNB 83.00 bkl Kok, Nils verfasserin aut Big Data in Real Estate? From Manual Appraisal to Automated Valuation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. In this article, the authors provide a practical application of big data by employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. The authors find strong evidence of the superiority of automated valuation models over traditional appraisals: The absolute error of the automated model is 9%, which compares favorably against the accuracy of traditional appraisals, and the model can produce an instant value at every moment in time at a very low cost. The authors also provide evidence of the importance of using hyperlocal information on the location of an asset. The model developed in this article is directly applicable for real estate lenders and investors and has important implications for the traditional appraisal industry. Commercial real estate Economic aspects Market capitalization Analysis Valuation Real property Artificial intelligence Asset allocation Product differentiation Automation Studies Research Real estate appraisal Real estate financing Koponen, Eija-Leena oth Martinez-Barbosa, Carmen Adriana oth Enthalten in The journal of portfolio management New York, NY : Pageant Media Ltd., 1974 43(2017), 6, Seite 202 (DE-627)129449377 (DE-600)197145-1 (DE-576)014815338 0095-4918 nnns volume:43 year:2017 number:6 pages:202 http://dx.doi.org/10.3905/jpm.2017.43.6.202 Volltext https://search.proquest.com/docview/1943019770 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_21 GBV_ILN_26 GBV_ILN_62 GBV_ILN_65 GBV_ILN_285 GBV_ILN_2009 GBV_ILN_2026 GBV_ILN_4012 GBV_ILN_4125 GBV_ILN_4310 GBV_ILN_4322 83.00 AVZ AR 43 2017 6 202 |
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Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. In this article, the authors provide a practical application of big data by employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. The authors find strong evidence of the superiority of automated valuation models over traditional appraisals: The absolute error of the automated model is 9%, which compares favorably against the accuracy of traditional appraisals, and the model can produce an instant value at every moment in time at a very low cost. The authors also provide evidence of the importance of using hyperlocal information on the location of an asset. The model developed in this article is directly applicable for real estate lenders and investors and has important implications for the traditional appraisal industry. |
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Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. In this article, the authors provide a practical application of big data by employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. The authors find strong evidence of the superiority of automated valuation models over traditional appraisals: The absolute error of the automated model is 9%, which compares favorably against the accuracy of traditional appraisals, and the model can produce an instant value at every moment in time at a very low cost. The authors also provide evidence of the importance of using hyperlocal information on the location of an asset. The model developed in this article is directly applicable for real estate lenders and investors and has important implications for the traditional appraisal industry. |
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Real estate is the third-largest asset class for institutional investors, but determining the value of commercial real estate assets remains elusively hard. In this article, the authors provide a practical application of big data by employing a unique set of data on U.S. multifamily assets, in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. The authors find strong evidence of the superiority of automated valuation models over traditional appraisals: The absolute error of the automated model is 9%, which compares favorably against the accuracy of traditional appraisals, and the model can produce an instant value at every moment in time at a very low cost. The authors also provide evidence of the importance of using hyperlocal information on the location of an asset. The model developed in this article is directly applicable for real estate lenders and investors and has important implications for the traditional appraisal industry. |
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