Bayesian Estimation of the Hierarchical SLX Model with an Application to Housing Markets
Abstract Hierarchical or multilevel models have long been used in hedonic models to delineate housing submarket boundaries in order to improve model accuracy. School districts are one important delineator of housing submarkets in an MSA. Spatial hedonic models have been extensively employed to deal...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hall, Joshua C. [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© Academy of Economics and Finance 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of economics and finance - Springer US, 1992, 46(2022), 2 vom: 22. Jan., Seite 360-373 |
|---|---|
| Übergeordnetes Werk: |
volume:46 ; year:2022 ; number:2 ; day:22 ; month:01 ; pages:360-373 |
| Links: |
|---|
| DOI / URN: |
10.1007/s12197-021-09568-2 |
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OLC2078492094 |
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| 520 | |a Abstract Hierarchical or multilevel models have long been used in hedonic models to delineate housing submarket boundaries in order to improve model accuracy. School districts are one important delineator of housing submarkets in an MSA. Spatial hedonic models have been extensively employed to deal with unobserved spatial heterogeneity and spatial spillovers. In this paper, we develop the spatially lagged X (or SLX) hierarchical model to integrate these two approaches to better understanding local housing markets. We apply the SLX hierarchical model to housing and school district test score data from Cincinnati Ohio. Our results highlight the importance of accounting for spatial spillovers and the fact that houses are embedded in school districts which vary in quality. | ||
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10.1007/s12197-021-09568-2 doi (DE-627)OLC2078492094 (DE-He213)s12197-021-09568-2-p DE-627 ger DE-627 rakwb eng 330 VZ Hall, Joshua C. verfasserin (orcid)0000-0001-6890-7387 aut Bayesian Estimation of the Hierarchical SLX Model with an Application to Housing Markets 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Academy of Economics and Finance 2021 Abstract Hierarchical or multilevel models have long been used in hedonic models to delineate housing submarket boundaries in order to improve model accuracy. School districts are one important delineator of housing submarkets in an MSA. Spatial hedonic models have been extensively employed to deal with unobserved spatial heterogeneity and spatial spillovers. In this paper, we develop the spatially lagged X (or SLX) hierarchical model to integrate these two approaches to better understanding local housing markets. We apply the SLX hierarchical model to housing and school district test score data from Cincinnati Ohio. Our results highlight the importance of accounting for spatial spillovers and the fact that houses are embedded in school districts which vary in quality. Spatial econometrics SLX model Bayesian methods Spatial hierarchical models Lacombe, Donald J. aut Neto, Amir aut Young, James aut Enthalten in Journal of economics and finance Springer US, 1992 46(2022), 2 vom: 22. Jan., Seite 360-373 (DE-627)171188756 (DE-600)1163091-7 (DE-576)357225511 1055-0925 nnns volume:46 year:2022 number:2 day:22 month:01 pages:360-373 https://doi.org/10.1007/s12197-021-09568-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_2153 AR 46 2022 2 22 01 360-373 |
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10.1007/s12197-021-09568-2 doi (DE-627)OLC2078492094 (DE-He213)s12197-021-09568-2-p DE-627 ger DE-627 rakwb eng 330 VZ Hall, Joshua C. verfasserin (orcid)0000-0001-6890-7387 aut Bayesian Estimation of the Hierarchical SLX Model with an Application to Housing Markets 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Academy of Economics and Finance 2021 Abstract Hierarchical or multilevel models have long been used in hedonic models to delineate housing submarket boundaries in order to improve model accuracy. School districts are one important delineator of housing submarkets in an MSA. Spatial hedonic models have been extensively employed to deal with unobserved spatial heterogeneity and spatial spillovers. In this paper, we develop the spatially lagged X (or SLX) hierarchical model to integrate these two approaches to better understanding local housing markets. We apply the SLX hierarchical model to housing and school district test score data from Cincinnati Ohio. Our results highlight the importance of accounting for spatial spillovers and the fact that houses are embedded in school districts which vary in quality. Spatial econometrics SLX model Bayesian methods Spatial hierarchical models Lacombe, Donald J. aut Neto, Amir aut Young, James aut Enthalten in Journal of economics and finance Springer US, 1992 46(2022), 2 vom: 22. Jan., Seite 360-373 (DE-627)171188756 (DE-600)1163091-7 (DE-576)357225511 1055-0925 nnns volume:46 year:2022 number:2 day:22 month:01 pages:360-373 https://doi.org/10.1007/s12197-021-09568-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_2153 AR 46 2022 2 22 01 360-373 |
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10.1007/s12197-021-09568-2 doi (DE-627)OLC2078492094 (DE-He213)s12197-021-09568-2-p DE-627 ger DE-627 rakwb eng 330 VZ Hall, Joshua C. verfasserin (orcid)0000-0001-6890-7387 aut Bayesian Estimation of the Hierarchical SLX Model with an Application to Housing Markets 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Academy of Economics and Finance 2021 Abstract Hierarchical or multilevel models have long been used in hedonic models to delineate housing submarket boundaries in order to improve model accuracy. School districts are one important delineator of housing submarkets in an MSA. Spatial hedonic models have been extensively employed to deal with unobserved spatial heterogeneity and spatial spillovers. In this paper, we develop the spatially lagged X (or SLX) hierarchical model to integrate these two approaches to better understanding local housing markets. We apply the SLX hierarchical model to housing and school district test score data from Cincinnati Ohio. Our results highlight the importance of accounting for spatial spillovers and the fact that houses are embedded in school districts which vary in quality. Spatial econometrics SLX model Bayesian methods Spatial hierarchical models Lacombe, Donald J. aut Neto, Amir aut Young, James aut Enthalten in Journal of economics and finance Springer US, 1992 46(2022), 2 vom: 22. Jan., Seite 360-373 (DE-627)171188756 (DE-600)1163091-7 (DE-576)357225511 1055-0925 nnns volume:46 year:2022 number:2 day:22 month:01 pages:360-373 https://doi.org/10.1007/s12197-021-09568-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_2153 AR 46 2022 2 22 01 360-373 |
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10.1007/s12197-021-09568-2 doi (DE-627)OLC2078492094 (DE-He213)s12197-021-09568-2-p DE-627 ger DE-627 rakwb eng 330 VZ Hall, Joshua C. verfasserin (orcid)0000-0001-6890-7387 aut Bayesian Estimation of the Hierarchical SLX Model with an Application to Housing Markets 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Academy of Economics and Finance 2021 Abstract Hierarchical or multilevel models have long been used in hedonic models to delineate housing submarket boundaries in order to improve model accuracy. School districts are one important delineator of housing submarkets in an MSA. Spatial hedonic models have been extensively employed to deal with unobserved spatial heterogeneity and spatial spillovers. In this paper, we develop the spatially lagged X (or SLX) hierarchical model to integrate these two approaches to better understanding local housing markets. We apply the SLX hierarchical model to housing and school district test score data from Cincinnati Ohio. Our results highlight the importance of accounting for spatial spillovers and the fact that houses are embedded in school districts which vary in quality. Spatial econometrics SLX model Bayesian methods Spatial hierarchical models Lacombe, Donald J. aut Neto, Amir aut Young, James aut Enthalten in Journal of economics and finance Springer US, 1992 46(2022), 2 vom: 22. Jan., Seite 360-373 (DE-627)171188756 (DE-600)1163091-7 (DE-576)357225511 1055-0925 nnns volume:46 year:2022 number:2 day:22 month:01 pages:360-373 https://doi.org/10.1007/s12197-021-09568-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_2153 AR 46 2022 2 22 01 360-373 |
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Abstract Hierarchical or multilevel models have long been used in hedonic models to delineate housing submarket boundaries in order to improve model accuracy. School districts are one important delineator of housing submarkets in an MSA. Spatial hedonic models have been extensively employed to deal with unobserved spatial heterogeneity and spatial spillovers. In this paper, we develop the spatially lagged X (or SLX) hierarchical model to integrate these two approaches to better understanding local housing markets. We apply the SLX hierarchical model to housing and school district test score data from Cincinnati Ohio. Our results highlight the importance of accounting for spatial spillovers and the fact that houses are embedded in school districts which vary in quality. © Academy of Economics and Finance 2021 |
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Abstract Hierarchical or multilevel models have long been used in hedonic models to delineate housing submarket boundaries in order to improve model accuracy. School districts are one important delineator of housing submarkets in an MSA. Spatial hedonic models have been extensively employed to deal with unobserved spatial heterogeneity and spatial spillovers. In this paper, we develop the spatially lagged X (or SLX) hierarchical model to integrate these two approaches to better understanding local housing markets. We apply the SLX hierarchical model to housing and school district test score data from Cincinnati Ohio. Our results highlight the importance of accounting for spatial spillovers and the fact that houses are embedded in school districts which vary in quality. © Academy of Economics and Finance 2021 |
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Abstract Hierarchical or multilevel models have long been used in hedonic models to delineate housing submarket boundaries in order to improve model accuracy. School districts are one important delineator of housing submarkets in an MSA. Spatial hedonic models have been extensively employed to deal with unobserved spatial heterogeneity and spatial spillovers. In this paper, we develop the spatially lagged X (or SLX) hierarchical model to integrate these two approaches to better understanding local housing markets. We apply the SLX hierarchical model to housing and school district test score data from Cincinnati Ohio. Our results highlight the importance of accounting for spatial spillovers and the fact that houses are embedded in school districts which vary in quality. © Academy of Economics and Finance 2021 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2078492094</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506010406.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12197-021-09568-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2078492094</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s12197-021-09568-2-p</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">330</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hall, Joshua C.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-6890-7387</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bayesian Estimation of the Hierarchical SLX Model with an Application to Housing Markets</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Academy of Economics and Finance 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Hierarchical or multilevel models have long been used in hedonic models to delineate housing submarket boundaries in order to improve model accuracy. 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