This paper investigates the incentives of agents working with buyers (buying agents) under the fixed percentage commission system (FPCS) and the implications on housing market outcomes. Our model shows that the FPCS without a binding contract between the buyer and the buying agent could produce outcomes that are more equitable for buyers. The reason is that the absence of a binding contract helps mitigate the conflict of interest between the buyer with her agent and ensures a more faithful behavior of the buying agent. Our model shows that agent heterogeneity plays an important role in determining the binding force of the FPCS in the absence of a binding contract. Results from simulations and empirical analyses using house transactions in Canada support our model predictions.
{"title":"Conflicts of Interest and Agent Heterogeneity in Buyer Brokerage","authors":"L. Kryzanowski, Yanting Wu, Tingyu Zhou","doi":"10.2139/ssrn.3805973","DOIUrl":"https://doi.org/10.2139/ssrn.3805973","url":null,"abstract":"This paper investigates the incentives of agents working with buyers (buying agents) under the fixed percentage commission system (FPCS) and the implications on housing market outcomes. Our model shows that the FPCS without a binding contract between the buyer and the buying agent could produce outcomes that are more equitable for buyers. The reason is that the absence of a binding contract helps mitigate the conflict of interest between the buyer with her agent and ensures a more faithful behavior of the buying agent. Our model shows that agent heterogeneity plays an important role in determining the binding force of the FPCS in the absence of a binding contract. Results from simulations and empirical analyses using house transactions in Canada support our model predictions.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76471622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While the outbreak of the COVID-19 disease has caused asset markets to experience an unprecedented spike of risk and uncertainty worldwide, the real estate market in many global cities appears to be immune to the adverse effects. How does COVID-19 affect urban housing markets? This study is a first attempt to identify the pandemic’s impact on house prices by applying a price gradient analysis to the COVID-19 epicentre in China. Considering microlevel housing transaction data in 62 areas from nine districts in Wuhan City from January 2019 to July 2020, the hedonic pricing and the price gradient models suggest that there was, respectively, a 4.8% and a 5.0–7.0% year-on-year fall in house prices immediately after the pandemic outbreak. Although house prices rebounded after the lockdown period, the gradient models show that the price gradients were flattened from the epicentre to the urban peripherals. The price premiums in high-density areas were also substantially discounted after the city’s lockdown. Our findings are robust to different model specifications. The implication is that the risk associated with the pandemic is localised and transitory in nature. People may be able to internalise the risk by residing in low-density residential areas.
{"title":"Housing Market in the Time of Pandemic: A Price Gradient Analysis from the COVID-19 Epicentre in China","authors":"K. Cheung, Chung Yim Edward Yiu, Chuyi Xiong","doi":"10.3390/JRFM14030108","DOIUrl":"https://doi.org/10.3390/JRFM14030108","url":null,"abstract":"While the outbreak of the COVID-19 disease has caused asset markets to experience an unprecedented spike of risk and uncertainty worldwide, the real estate market in many global cities appears to be immune to the adverse effects. How does COVID-19 affect urban housing markets? This study is a first attempt to identify the pandemic’s impact on house prices by applying a price gradient analysis to the COVID-19 epicentre in China. Considering microlevel housing transaction data in 62 areas from nine districts in Wuhan City from January 2019 to July 2020, the hedonic pricing and the price gradient models suggest that there was, respectively, a 4.8% and a 5.0–7.0% year-on-year fall in house prices immediately after the pandemic outbreak. Although house prices rebounded after the lockdown period, the gradient models show that the price gradients were flattened from the epicentre to the urban peripherals. The price premiums in high-density areas were also substantially discounted after the city’s lockdown. Our findings are robust to different model specifications. The implication is that the risk associated with the pandemic is localised and transitory in nature. People may be able to internalise the risk by residing in low-density residential areas.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90592550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the institutional literature convincingly shows, socioeconomic phenomena are to a large extent shaped by the formal institutions, that is, legal acts (laws and ordinances). However, the latter are formulated in a specific language that is difficult to understand, let alone to measure. However, since the early 1990s, a whole branch of economic analysis of governmental regulations has evolved. It is known as leximetrics, i.e., the measuring of laws. It covers a wide range of economic sectors, such as financial, labor, housing, and product markets, among others. The two most popular methods are codification and surveys. Under the first method, the legal texts are analyzed, relevant provisions extracted, and numeric values assigned depending on these provisions. Under the surveys method, local experts are asked to provide their assessment of currently valid legal provisions and sometimes also their enforcement. In both cases, the legal texts are mapped onto real-valued indices with the objective of gauging the intensity of governmental regulations. These indices can be and are successfully used to explain the economic phenomena. This study provides a comprehensive overview of the leximetric literature and demonstrates interdependences between different types of governmental regulations.
{"title":"Measuring Unmeasurable: How to Map Laws to Numbers Using Leximetrics","authors":"K. Kholodilin, Linus Pfeiffer","doi":"10.2139/ssrn.3810489","DOIUrl":"https://doi.org/10.2139/ssrn.3810489","url":null,"abstract":"As the institutional literature convincingly shows, socioeconomic phenomena are to a large extent shaped by the formal institutions, that is, legal acts (laws and ordinances). However, the latter are formulated in a specific language that is difficult to understand, let alone to measure. However, since the early 1990s, a whole branch of economic analysis of governmental regulations has evolved. It is known as leximetrics, i.e., the measuring of laws. It covers a wide range of economic sectors, such as financial, labor, housing, and product markets, among others. The two most popular methods are codification and surveys. Under the first method, the legal texts are analyzed, relevant provisions extracted, and numeric values assigned depending on these provisions. Under the surveys method, local experts are asked to provide their assessment of currently valid legal provisions and sometimes also their enforcement. In both cases, the legal texts are mapped onto real-valued indices with the objective of gauging the intensity of governmental regulations. These indices can be and are successfully used to explain the economic phenomena. This study provides a comprehensive overview of the leximetric literature and demonstrates interdependences between different types of governmental regulations.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73562278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a mixed frequency repeat sales model for commercial real estate, taking into account changes in net operating income between the date of buying and selling the property. Moreover, we relate monthly private market index asset returns to lags, up to one year, of daily REIT returns. The underlying REIT model enables us to interpolate the monthly private market index returns on a daily basis, and to predict the private market index asset returns going forward. The mixed frequency repeat sales model facilitates daily appraisal of commercial real estate portfolios. We apply the model on sale prices (all property types, and apartments only) in the period January 2006 up to July 2020, provided to us by Real Capital Analytics. We find that the mixed frequency repeat sales model reduces one-month-ahead forecasts errors and index revisions, compared to a benchmark model without daily REIT returns.
我们提出了一个混合频率重复销售模型的商业房地产，考虑到在购买和出售财产之间的净营业收入的变化。此外，我们将月度私人市场指数资产回报与房地产投资信托基金每日回报的滞后(最长一年)联系起来。基础的房地产投资信托基金模型使我们能够以每日为基础插值私人市场指数的月度回报，并预测私人市场指数资产未来的回报。混合频率重复销售模型有助于商业房地产投资组合的日常评估。我们将该模型应用于2006年1月至2020年7月期间的销售价格(所有房产类型，仅限公寓)，该模型由Real Capital Analytics提供。我们发现，与没有每日REIT回报的基准模型相比，混合频率重复销售模型减少了一个月前的预测误差和指数修正。
{"title":"Daily Appraisal of Commercial Real Estate A New Mixed Frequency Approach","authors":"Marc K. Francke, A. Minne","doi":"10.2139/ssrn.3789205","DOIUrl":"https://doi.org/10.2139/ssrn.3789205","url":null,"abstract":"We present a mixed frequency repeat sales model for commercial real estate, taking into account changes in net operating income between the date of buying and selling the property. Moreover, we relate monthly private market index asset returns to lags, up to one year, of daily REIT returns. The underlying REIT model enables us to interpolate the monthly private market index returns on a daily basis, and to predict the private market index asset returns going forward. The mixed frequency repeat sales model facilitates daily appraisal of commercial real estate portfolios. We apply the model on sale prices (all property types, and apartments only) in the period January 2006 up to July 2020, provided to us by Real Capital Analytics. We find that the mixed frequency repeat sales model reduces one-month-ahead forecasts errors and index revisions, compared to a benchmark model without daily REIT returns.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83117088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We offer the first large scale descriptive study of residential leases, based on a dataset of ~170,000 residential leases filed in support of over ~200,000 Philadelphia eviction proceedings from 2005 through 2019. These leases are highly likely to contain unenforceable terms, and their pro-landlord tilt has increased sharply over time. Matching leases with individual tenant characteristics, we show that unlawful terms are surprisingly likely to be associated with more expensive leaseholds in richer, whiter parts of the city. This result is linked to landlords' growing adoption of shared forms, originally created by non-profit landlord associations, and more recently available online for a nominal fee. Generally, such shared form leases contain worse rules for tenants than the proprietary leases they replace. Over time, it has become easier and cheaper for landlords to adopt such common forms, meaning that access to justice for landlords strips tenants of rights. We observe few within landlord effects: rather, property owners specialize in particular areas in the city. This specialization leads black tenants to be more susceptible to eviction based on crime or drug use on the premises, an effect concentrated in whiter neighborhoods. Our results offer a significant advance in the empirical study of consumer contracting, building the field by examining individual differences in adherents, geography-effects, information costs and time trends.
{"title":"Leases as Forms","authors":"David Hoffman, Anton Strezhnev","doi":"10.2139/ssrn.3786326","DOIUrl":"https://doi.org/10.2139/ssrn.3786326","url":null,"abstract":"We offer the first large scale descriptive study of residential leases, based on a dataset of ~170,000 residential leases filed in support of over ~200,000 Philadelphia eviction proceedings from 2005 through 2019. These leases are highly likely to contain unenforceable terms, and their pro-landlord tilt has increased sharply over time. Matching leases with individual tenant characteristics, we show that unlawful terms are surprisingly likely to be associated with more expensive leaseholds in richer, whiter parts of the city. This result is linked to landlords' growing adoption of shared forms, originally created by non-profit landlord associations, and more recently available online for a nominal fee. \u0000 \u0000Generally, such shared form leases contain worse rules for tenants than the proprietary leases they replace. Over time, it has become easier and cheaper for landlords to adopt such common forms, meaning that access to justice for landlords strips tenants of rights. We observe few within landlord effects: rather, property owners specialize in particular areas in the city. This specialization leads black tenants to be more susceptible to eviction based on crime or drug use on the premises, an effect concentrated in whiter neighborhoods. Our results offer a significant advance in the empirical study of consumer contracting, building the field by examining individual differences in adherents, geography-effects, information costs and time trends.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90599286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines the effect of mainland Chinese buyers' housing purchase in Hong Kong. Contrary to media's allegation on mainland buyers causing huge bubbles in Hong Kong housing market, we find that mainland buyers only constitute less than 4% of housing transactions in Hong Kong from 2001 to 2017, and their price premium over locals is only 1.4% on average. The mainland premium is higher for properties attracting more interests from mainland buyers, such as luxury units larger in size (3.52%). We also find that the price premium varies with hedging demand for the currency risk over time (safe haven effect). Furthermore, the price premium is higher in buildings with more existing mainland homeowners (residential sorting). At last, the price premium is lower if the mainland buyer has stronger bargaining power such as more prior transaction experience or facing a mainland seller.
{"title":"A Tale of Two Cities: Mainland Chinese Buyers in Hong Kong Housing Market","authors":"Yi Fan, M. Hu, Wayne Xinwei Wan, Zhenping Wang","doi":"10.2139/ssrn.3477421","DOIUrl":"https://doi.org/10.2139/ssrn.3477421","url":null,"abstract":"This paper examines the effect of mainland Chinese buyers' housing purchase in Hong Kong. Contrary to media's allegation on mainland buyers causing huge bubbles in Hong Kong housing market, we find that mainland buyers only constitute less than 4% of housing transactions in Hong Kong from 2001 to 2017, and their price premium over locals is only 1.4% on average. The mainland premium is higher for properties attracting more interests from mainland buyers, such as luxury units larger in size (3.52%). We also find that the price premium varies with hedging demand for the currency risk over time (safe haven effect). Furthermore, the price premium is higher in buildings with more existing mainland homeowners (residential sorting). At last, the price premium is lower if the mainland buyer has stronger bargaining power such as more prior transaction experience or facing a mainland seller.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85316278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Bekkerman, Maxime C. Cohen, J. Maiden, Dmitry Mitrofanov
The U.S. Tax Cuts and Jobs Act of 2017 introduced opportunity zones (OZs). This program provides tax benefits for real estate investments in designated census tracts, with the goal of fostering economic development in distressed neighborhoods. We examine the impact of OZs on residential real estate by exploiting two datasets: a proprietary real estate dataset and census-tract demographics data between 2010 and 2019. Our real estate dataset comprises 36.1 million residential transactions spanning all 50 U.S. states. We first investigate the OZ selection process by examining which census-tract characteristics were taken into account. As expected, we find that tracts with higher poverty and unemployment rates and lower income levels were more likely to be selected. However, we find evidence that tracts with a higher average real estate price were also more likely to be selected. We then analyze the impact of the OZ program by comparing key real estate metrics (price and transaction volume) before and after the program launch. We find that the OZ program increased real estate prices by 4.03-6.13%, but we do not observe a significant effect on the transaction volume. Finally, we examine the moderating effects of census tract characteristics on the impact of the OZ program. Interestingly, we show that the price increase is driven by the higher end of the OZ market. Our findings thus question the overall societal and economic benefits of the program.
{"title":"The Impact of the Opportunity Zone Program on the Residential Real Estate Market","authors":"R. Bekkerman, Maxime C. Cohen, J. Maiden, Dmitry Mitrofanov","doi":"10.2139/ssrn.3780241","DOIUrl":"https://doi.org/10.2139/ssrn.3780241","url":null,"abstract":"The U.S. Tax Cuts and Jobs Act of 2017 introduced opportunity zones (OZs). This program provides tax benefits for real estate investments in designated census tracts, with the goal of fostering economic development in distressed neighborhoods. We examine the impact of OZs on residential real estate by exploiting two datasets: a proprietary real estate dataset and census-tract demographics data between 2010 and 2019. Our real estate dataset comprises 36.1 million residential transactions spanning all 50 U.S. states. We first investigate the OZ selection process by examining which census-tract characteristics were taken into account. As expected, we find that tracts with higher poverty and unemployment rates and lower income levels were more likely to be selected. However, we find evidence that tracts with a higher average real estate price were also more likely to be selected. We then analyze the impact of the OZ program by comparing key real estate metrics (price and transaction volume) before and after the program launch. We find that the OZ program increased real estate prices by 4.03-6.13%, but we do not observe a significant effect on the transaction volume. Finally, we examine the moderating effects of census tract characteristics on the impact of the OZ program. Interestingly, we show that the price increase is driven by the higher end of the OZ market. Our findings thus question the overall societal and economic benefits of the program.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73285749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a result of the liquidity crisis in October 2008, 18 open-end real estate funds with a total volume of €26 billion in assets under management had to be liquidated. Many shareholders decided to sell their shares on the secondary market instead of awaiting the iterative liquidation of the fund assets. This paper estimates and explains the returns of these secondary market trades based on a unique dataset comprising secondary market prices and individual fund characteristics. The estimated returns exhibit an enormous variation across the funds and transaction dates. A subsiding panel regression demonstrates that the return variation can be attributed to the price-discount at which the shares traded on the secondary market and the composition of the remaining fund assets.
{"title":"The Return on Secondary Market Trades of Open-end Real Estate Funds in Liquidation","authors":"P. Gerlach","doi":"10.2139/ssrn.3773723","DOIUrl":"https://doi.org/10.2139/ssrn.3773723","url":null,"abstract":"As a result of the liquidity crisis in October 2008, 18 open-end real estate funds with a total volume of €26 billion in assets under management had to be liquidated. Many shareholders decided to sell their shares on the secondary market instead of awaiting the iterative liquidation of the fund assets. This paper estimates and explains the returns of these secondary market trades based on a unique dataset comprising secondary market prices and individual fund characteristics. The estimated returns exhibit an enormous variation across the funds and transaction dates. A subsiding panel regression demonstrates that the return variation can be attributed to the price-discount at which the shares traded on the secondary market and the composition of the remaining fund assets.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90680221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A rapidly expanding universe of technology-focused startups is trying to change and improve the way real estate markets operate. The undisputed predictive power of machine learning (ML) models often plays a crucial role in the 'disruption' of traditional processes. However, an accountability gap prevails: How do the models arrive at their predictions? Do they do what we hope they do – or are corners cut?
Training ML models is a software development process at heart. We suggest following the dedicated software testing framework and verifying that the ML model is performing as intended. Illustratively, we augment two image classifiers with a system testing procedure based on local interpretable model-agnostic explanation (LIME) techniques. Analyzing the classifications sheds light on some of the factors that determine the behavior of the systems. We show that cross-validation is simply not good enough when operating in regulated environments.
{"title":"Towards Accountability in Machine Learning Applications: A System-testing Approach","authors":"Wayne Xinwei Wan, Thies Lindenthal","doi":"10.2139/ssrn.3758451","DOIUrl":"https://doi.org/10.2139/ssrn.3758451","url":null,"abstract":"A rapidly expanding universe of technology-focused startups is trying to change and improve the way real estate markets operate. The undisputed predictive power of machine learning (ML) models often plays a crucial role in the 'disruption' of traditional processes. However, an accountability gap prevails: How do the models arrive at their predictions? Do they do what we hope they do – or are corners cut?<br><br>Training ML models is a software development process at heart. We suggest following the dedicated software testing framework and verifying that the ML model is performing as intended. Illustratively, we augment two image classifiers with a system testing procedure based on local interpretable model-agnostic explanation (LIME) techniques. Analyzing the classifications sheds light on some of the factors that determine the behavior of the systems. We show that cross-validation is simply not good enough when operating in regulated environments.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82522766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The co-movement of buyers and vacancies, i.e. the Beveridge Curve, is a key determinant of the cyclical properties of the housing market. It determines the sign of the correlation between prices and key measures of liquidity such as vacancies (i.e. houses for sale), sales, and time-to-sell. As recent work has shown, to account for the core stylized facts of the housing market, search and matching models must be consistent with a positively correlated co-movement of buyers and vacancies—the Beveridge Curve must be upward-sloping. This paper provides evidence that buyers and vacancies are indeed positively correlated along the housing cycle, i.e. the Beveridge Curve on the housing market is upward sloping. Using data on vacancies and time-to-sell, we construct a series for buyers and estimate the slope of the Beveridge Curve. This approach requires only one minimal structural assumption: the existence of a matching function. The regression results confirm the positive relationship between buyers and vacancies over the business cycle. In addition, we provide an estimate of the elasticity of vacancies with respect to buyers. A one percent increase in vacancies is associated with around a two percent increase in buyers, confirming recent findings that buyers are more volatile than houses for sale. We hope this estimate will help future researchers in this area.
{"title":"On the Slope of the Beveridge Curve in the Housing Market","authors":"Miroslav Gabrovski, Victor Ortego-Marti","doi":"10.2139/ssrn.3932080","DOIUrl":"https://doi.org/10.2139/ssrn.3932080","url":null,"abstract":"The co-movement of buyers and vacancies, i.e. the Beveridge Curve, is a key determinant of the cyclical properties of the housing market. It determines the sign of the correlation between prices and key measures of liquidity such as vacancies (i.e. houses for sale), sales, and time-to-sell. As recent work has shown, to account for the core stylized facts of the housing market, search and matching models must be consistent with a positively correlated co-movement of buyers and vacancies—the Beveridge Curve must be upward-sloping. This paper provides evidence that buyers and vacancies are indeed positively correlated along the housing cycle, i.e. the Beveridge Curve on the housing market is upward sloping. Using data on vacancies and time-to-sell, we construct a series for buyers and estimate the slope of the Beveridge Curve. This approach requires only one minimal structural assumption: the existence of a matching function. The regression results confirm the positive relationship between buyers and vacancies over the business cycle. In addition, we provide an estimate of the elasticity of vacancies with respect to buyers. A one percent increase in vacancies is associated with around a two percent increase in buyers, confirming recent findings that buyers are more volatile than houses for sale. We hope this estimate will help future researchers in this area.","PeriodicalId":21047,"journal":{"name":"Real Estate eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79483927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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