In this article, we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will be used to estimate a common trend, property type trends, location value, and property random effects for properties that sold more than once. The machine learning algorithm will fit the observed characteristics (features) in a complex nonlinear fashion. The model is applied to a small sample of 2652 transactions in Phoenix (AZ) between 2001 and 2021. We only observe a limited number of property characteristics. The average out‐of‐sample MAPE is below 11%, which is as good or even better compared to the average appraisal error found in literature. The out‐of‐sample MAPE is even 9% for properties that sold more than once in the training set. In addition, our model provides indexes and locational heatmaps. These have their own uses and cannot be obtained with standard machine learning algorithms.
{"title":"Combining machine learning and econometrics: Application to commercial real estate prices","authors":"Marc K. Francke, Alex van de Minne","doi":"10.1111/1540-6229.12483","DOIUrl":"https://doi.org/10.1111/1540-6229.12483","url":null,"abstract":"In this article, we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will be used to estimate a common trend, property type trends, location value, and property random effects for properties that sold more than once. The machine learning algorithm will fit the observed characteristics (features) in a complex nonlinear fashion. The model is applied to a small sample of 2652 transactions in Phoenix (AZ) between 2001 and 2021. We only observe a limited number of property characteristics. The average out‐of‐sample MAPE is below 11%, which is as good or even better compared to the average appraisal error found in literature. The out‐of‐sample MAPE is even 9% for properties that sold more than once in the training set. In addition, our model provides indexes and locational heatmaps. These have their own uses and cannot be obtained with standard machine learning algorithms.","PeriodicalId":47731,"journal":{"name":"Real Estate Economics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140382931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dragana Cvijanović, Lyndsey Rolheiser, Alex Van de Minne
We analyze the causal effect of air pollution (acute fine particulate matter) exposure on the commercial real estate (CRE) market. We instrument for air pollution using changes in local wind direction to find that an increase in fine particulate matter exposure leads to a contemporaneous decrease in CRE market values and (net) income as well as an increase in capital expenditures. Heterogeneous treatment analysis within a building-level fixed effects framework uncovers that the negative effect on market values is concentrated in the office sector, consistent with the notion that air pollution-induced decreases in CRE values are driven by a reduction in CRE assets’ productive capacity. Additionally, we document that the negative impact on (net) income is concentrated in the apartment sector, which is consistent with a broad set of local disamenity mechanisms identified in previous residential real estate literature.
{"title":"Commercial real estate and air pollution","authors":"Dragana Cvijanović, Lyndsey Rolheiser, Alex Van de Minne","doi":"10.1111/1540-6229.12484","DOIUrl":"https://doi.org/10.1111/1540-6229.12484","url":null,"abstract":"We analyze the causal effect of air pollution (acute fine particulate matter) exposure on the commercial real estate (CRE) market. We instrument for air pollution using changes in local wind direction to find that an increase in fine particulate matter exposure leads to a contemporaneous decrease in CRE market values and (net) income as well as an increase in capital expenditures. Heterogeneous treatment analysis within a building-level fixed effects framework uncovers that the negative effect on market values is concentrated in the office sector, consistent with the notion that air pollution-induced decreases in CRE values are driven by a reduction in CRE assets’ productive capacity. Additionally, we document that the negative impact on (net) income is concentrated in the apartment sector, which is consistent with a broad set of local disamenity mechanisms identified in previous residential real estate literature.","PeriodicalId":47731,"journal":{"name":"Real Estate Economics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140171353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We provide a US national portrait of annual average exposure to floods across racial/ethnic and income groups, using predictions from the First Street Foundation flooding exposure model. Nationally, we find that Native Americans in inland neighborhoods and Hispanics in coastal ones face (statistically) significantly higher average exposure to flooding than non‐Hispanic Whites, even when neighborhood income composition is controlled. Surprisingly, non‐Hispanic Blacks and Asians generally have significantly lower average exposure to floods than non‐Hispanic Whites. Lower income groups exhibit substantially higher exposure in inland areas than higher income groups—but not in coastal areas—when neighborhood racial/ethnic composition is controlled.
我们利用 First Street 基金会洪水风险模型的预测结果，描绘了美国全国不同种族/族裔和收入群体的年平均洪水风险。我们发现，在全国范围内，内陆社区的美国原住民和沿海社区的西班牙裔美国人面临的洪灾平均风险（在统计上）明显高于非西班牙裔白人，即使在控制社区收入构成的情况下也是如此。令人惊讶的是，非西班牙裔黑人和亚裔的平均洪灾风险一般明显低于非西班牙裔白人。在控制了邻里种族/族裔构成的情况下，低收入群体在内陆地区的洪灾风险大大高于高收入群体，但在沿海地区则不然。
{"title":"The color of water: Racial and income differences in exposure to floods across US neighborhoods","authors":"George C. Galster, Joshua Galster, Karl Vachuska","doi":"10.1111/1540-6229.12480","DOIUrl":"https://doi.org/10.1111/1540-6229.12480","url":null,"abstract":"We provide a US national portrait of annual average exposure to floods across racial/ethnic and income groups, using predictions from the First Street Foundation flooding exposure model. Nationally, we find that Native Americans in inland neighborhoods and Hispanics in coastal ones face (statistically) significantly higher average exposure to flooding than non‐Hispanic Whites, even when neighborhood income composition is controlled. Surprisingly, non‐Hispanic Blacks and Asians generally have significantly lower average exposure to floods than non‐Hispanic Whites. Lower income groups exhibit substantially higher exposure in inland areas than higher income groups—but not in coastal areas—when neighborhood racial/ethnic composition is controlled.","PeriodicalId":47731,"journal":{"name":"Real Estate Economics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140037465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using the Credit Risk Transfers (CRTs) issued by Fannie Mae and Freddie Mac, we study how, absent government intervention, mortgage markets would price hurricane risk. Currently, such risk is priced equally across locations even if it is location-specific. We hand collect a novel and detailed database to exploit CRTs' heterogeneous exposure to Hurricanes Harvey and Irma. Using a diff-in-diff specification, we estimate the reaction of private investors to hurricane risk. We use the previous results to calibrate a model of mortgage lending. We simulate hurricane frequencies and mortgage default probabilities in each US county to derive the market price of mortgage credit risk, that is, the implied guarantee fees (g-fees). Market-implied g-fees in counties most exposed to hurricanes would be 70% higher than inland counties.
{"title":"Climate risk in mortgage markets: Evidence from Hurricanes Harvey and Irma","authors":"Pedro Gete, Athena Tsouderou, Susan M. Wachter","doi":"10.1111/1540-6229.12477","DOIUrl":"https://doi.org/10.1111/1540-6229.12477","url":null,"abstract":"Using the Credit Risk Transfers (CRTs) issued by Fannie Mae and Freddie Mac, we study how, absent government intervention, mortgage markets would price hurricane risk. Currently, such risk is priced equally across locations even if it is location-specific. We hand collect a novel and detailed database to exploit CRTs' heterogeneous exposure to Hurricanes Harvey and Irma. Using a diff-in-diff specification, we estimate the reaction of private investors to hurricane risk. We use the previous results to calibrate a model of mortgage lending. We simulate hurricane frequencies and mortgage default probabilities in each US county to derive the market price of mortgage credit risk, that is, the implied guarantee fees (g-fees). Market-implied g-fees in counties most exposed to hurricanes would be 70% higher than inland counties.","PeriodicalId":47731,"journal":{"name":"Real Estate Economics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140006215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Considering the prevalent information asymmetry in housing markets, this study demonstrates the predictive power of homebuyers’ geographic proximity on housing prices. At the ZIP‐code level, a 10‐percentage‐point increase in the fraction of local buyers corresponds to a 1.1‐percentage‐point higher housing price growth over the subsequent 2 years. At the individual level, out‐of‐town buyers experience a 0.64‐percentage‐point lower annual return compared to local buyers within a county. These results not only highlight the significant information advantages enjoyed by geographically proximate buyers, but also imply that informationally privileged buyers’ revealed preferences for specific locations could provide informationally disadvantaged buyers with hints about which areas are likely to experience higher housing price growth in the near future.
{"title":"Homebuyers’ geographic proximity as a predictor of future housing price growth","authors":"Hayoung Kim","doi":"10.1111/1540-6229.12479","DOIUrl":"https://doi.org/10.1111/1540-6229.12479","url":null,"abstract":"Considering the prevalent information asymmetry in housing markets, this study demonstrates the predictive power of homebuyers’ geographic proximity on housing prices. At the ZIP‐code level, a 10‐percentage‐point increase in the fraction of local buyers corresponds to a 1.1‐percentage‐point higher housing price growth over the subsequent 2 years. At the individual level, out‐of‐town buyers experience a 0.64‐percentage‐point lower annual return compared to local buyers within a county. These results not only highlight the significant information advantages enjoyed by geographically proximate buyers, but also imply that informationally privileged buyers’ revealed preferences for specific locations could provide informationally disadvantaged buyers with hints about which areas are likely to experience higher housing price growth in the near future.","PeriodicalId":47731,"journal":{"name":"Real Estate Economics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139977804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We empirically document that the effectiveness of the German rent control introduced in 2015 in achieving rental housing affordability is limited. Exploring the reasons for this limited effectiveness, we focus on the impact of the rent control on the yield on rental housing investments proxied by rent‐price ratios, which we derive by predicting sale prices to rental objects based on a hedonic model using micro‐level quotes on rental and sale listing. Exploiting the temporal, regional, and object‐specific variation generated by the design of the rent control, we identify a causal negative effect of the rent control on the yield of rental objects subject to the regulation. Furthermore, we zoom into the spillovers across regulated objects and objects in the affected markets that were exempt from the regulation and find rising yields for the exempted objects, suggesting that the regulation contributed to gentrification via a shift of rental housing supply away from the regulated segment.
{"title":"Investment incentives of rent controls and gentrification: Evidence from German micro data","authors":"Vera Baye, Valeriya Dinger","doi":"10.1111/1540-6229.12478","DOIUrl":"https://doi.org/10.1111/1540-6229.12478","url":null,"abstract":"We empirically document that the effectiveness of the German rent control introduced in 2015 in achieving rental housing affordability is limited. Exploring the reasons for this limited effectiveness, we focus on the impact of the rent control on the yield on rental housing investments proxied by rent‐price ratios, which we derive by predicting sale prices to rental objects based on a hedonic model using micro‐level quotes on rental and sale listing. Exploiting the temporal, regional, and object‐specific variation generated by the design of the rent control, we identify a causal negative effect of the rent control on the yield of rental objects subject to the regulation. Furthermore, we zoom into the spillovers across regulated objects and objects in the affected markets that were exempt from the regulation and find rising yields for the exempted objects, suggesting that the regulation contributed to gentrification via a shift of rental housing supply away from the regulated segment.","PeriodicalId":47731,"journal":{"name":"Real Estate Economics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We empirically document that the effectiveness of the German rent control introduced in 2015 in achieving rental housing affordability is limited. Exploring the reasons for this limited effectiveness, we focus on the impact of the rent control on the yield on rental housing investments proxied by rent‐price ratios, which we derive by predicting sale prices to rental objects based on a hedonic model using micro‐level quotes on rental and sale listing. Exploiting the temporal, regional, and object‐specific variation generated by the design of the rent control, we identify a causal negative effect of the rent control on the yield of rental objects subject to the regulation. Furthermore, we zoom into the spillovers across regulated objects and objects in the affected markets that were exempt from the regulation and find rising yields for the exempted objects, suggesting that the regulation contributed to gentrification via a shift of rental housing supply away from the regulated segment.
{"title":"Investment incentives of rent controls and gentrification: Evidence from German micro data","authors":"Vera Baye, Valeriya Dinger","doi":"10.1111/1540-6229.12478","DOIUrl":"https://doi.org/10.1111/1540-6229.12478","url":null,"abstract":"We empirically document that the effectiveness of the German rent control introduced in 2015 in achieving rental housing affordability is limited. Exploring the reasons for this limited effectiveness, we focus on the impact of the rent control on the yield on rental housing investments proxied by rent‐price ratios, which we derive by predicting sale prices to rental objects based on a hedonic model using micro‐level quotes on rental and sale listing. Exploiting the temporal, regional, and object‐specific variation generated by the design of the rent control, we identify a causal negative effect of the rent control on the yield of rental objects subject to the regulation. Furthermore, we zoom into the spillovers across regulated objects and objects in the affected markets that were exempt from the regulation and find rising yields for the exempted objects, suggesting that the regulation contributed to gentrification via a shift of rental housing supply away from the regulated segment.","PeriodicalId":47731,"journal":{"name":"Real Estate Economics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article explores the determinants of floor area ratio (FAR) limit, a major form of construction density regulation, in China. I develop a spatial equilibrium framework to study local governments’ optimal FAR design and investigate over 400,000 residential land transactions between 2007 and 2019 to perform the empirical analysis. Exploiting the exogenous variations generated by administrative adjustments and applying a propensity score matching approach, I find that a one standard deviation increase in local budgetary revenue decreases FAR limits by 0.29. Further counterfactual analysis suggests that the land finance model contributes to housing affordability issues and spatial inequality in China.
{"title":"Low‐rise buildings in big cities: Theory and evidence from China","authors":"Xiao Yu","doi":"10.1111/1540-6229.12476","DOIUrl":"https://doi.org/10.1111/1540-6229.12476","url":null,"abstract":"This article explores the determinants of floor area ratio (FAR) limit, a major form of construction density regulation, in China. I develop a spatial equilibrium framework to study local governments’ optimal FAR design and investigate over 400,000 residential land transactions between 2007 and 2019 to perform the empirical analysis. Exploiting the exogenous variations generated by administrative adjustments and applying a propensity score matching approach, I find that a one standard deviation increase in local budgetary revenue decreases FAR limits by 0.29. Further counterfactual analysis suggests that the land finance model contributes to housing affordability issues and spatial inequality in China.","PeriodicalId":47731,"journal":{"name":"Real Estate Economics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139781821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article explores the determinants of floor area ratio (FAR) limit, a major form of construction density regulation, in China. I develop a spatial equilibrium framework to study local governments’ optimal FAR design and investigate over 400,000 residential land transactions between 2007 and 2019 to perform the empirical analysis. Exploiting the exogenous variations generated by administrative adjustments and applying a propensity score matching approach, I find that a one standard deviation increase in local budgetary revenue decreases FAR limits by 0.29. Further counterfactual analysis suggests that the land finance model contributes to housing affordability issues and spatial inequality in China.
{"title":"Low‐rise buildings in big cities: Theory and evidence from China","authors":"Xiao Yu","doi":"10.1111/1540-6229.12476","DOIUrl":"https://doi.org/10.1111/1540-6229.12476","url":null,"abstract":"This article explores the determinants of floor area ratio (FAR) limit, a major form of construction density regulation, in China. I develop a spatial equilibrium framework to study local governments’ optimal FAR design and investigate over 400,000 residential land transactions between 2007 and 2019 to perform the empirical analysis. Exploiting the exogenous variations generated by administrative adjustments and applying a propensity score matching approach, I find that a one standard deviation increase in local budgetary revenue decreases FAR limits by 0.29. Further counterfactual analysis suggests that the land finance model contributes to housing affordability issues and spatial inequality in China.","PeriodicalId":47731,"journal":{"name":"Real Estate Economics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article investigates the value of access to alcohol consumption by examining housing price changes after a liquor ban that was catalyzed by an unexpected riot in Singapore. The ban restricts alcohol consumption in liquor control zones further than in areas outside such zones. We find that the housing price changes in the liquor control zones were weak, which implies that the utility and disutility of alcohol consumption almost cancel each other out. In contrast, housing prices increased for houses within 800 m of but outside the liquor control zones. We investigate potential explanations for these phenomena.
{"title":"Alcohol consumption and the value of community","authors":"Xiaoyu Zhang, Yunqi Zhang","doi":"10.1111/1540-6229.12473","DOIUrl":"https://doi.org/10.1111/1540-6229.12473","url":null,"abstract":"This article investigates the value of access to alcohol consumption by examining housing price changes after a liquor ban that was catalyzed by an unexpected riot in Singapore. The ban restricts alcohol consumption in liquor control zones further than in areas outside such zones. We find that the housing price changes in the liquor control zones were weak, which implies that the utility and disutility of alcohol consumption almost cancel each other out. In contrast, housing prices increased for houses within 800 m of but outside the liquor control zones. We investigate potential explanations for these phenomena.","PeriodicalId":47731,"journal":{"name":"Real Estate Economics","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139677979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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