市场资产回报、经济条件和公司基本面对净租赁资本化率的影响

IF 1.2 4区 经济学 Q3 BUSINESS, FINANCE
Stace Sirmans, Stacy Sirmans, Greg Smersh, Daniel Winkler
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Using this unique dataset of more than 8,000 single-tenant net lease retail property transactions, we develop a quarterly cap rate index controlling for Metropolitan Statistical Area (MSA) and industry fixed effects, property and lease characteristics, and localized influences such as population density and household income. Third, we examine the effect of excess corporate bond spreads, excess stock returns, stock market indicators, firm financials, and economic and demographic indicators. Finally, we examine the effect on cap rates of MSA characteristics such as size, wealth, poverty, crime, gross domestic product, and growth. The findings show that, besides the systematic risk from stock and bond returns, national and metropolitan economic forces and firm fundamental factors explain variation in cap rates.Keywords: cap ratesnet leaseexcess returnmacroeconomic factors AcknowledgmentsWe thank the editor and the anonymous referees for their insightful feedback, which has substantially improved this article. We also thank the participants at the 2023 ARES Conference for their helpful comments.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 As a percentage of total investible wealth, real estate is a significant percentage of domestic publicly traded equities. According to Siblis Research Ltd, the total market capitalization of the U.S. stock market was $53.4 trillion as of December 31, 2021 (https://siblisresearch.com/data/us-stock-market-value/) while, according to the National Association of Real Estate Investment Trusts (NAREIT), the total value of U.S. commercial real estate was $20.7 trillion as of June 2021 (https://www.reit.com/data-research/research/nareit-research/estimating-size-commercial-real-estate-market-us-2021).2 The cap rate is defined as the ratio of a property’s net operating income to its current market value; most often the cap rate is based on expected net operating income.3 Common multifactor models in the finance literature include the Fama-French three-factor model (1992) and the arbitrage pricing theory (APT) model (Ross, Citation1976).4 All the studies discussed in this section use either the full cap rate or the excess cap rate (typically defined as the full cap rate minus the three-month TB yield) as the dependent variable in their analysis.5 To examine the relationship between real estate and capital markets, additional studies have used alternative return measures such as publicly traded REITs or National Council of Real Estate Investment Fiduciaries (NCREIF) returns data. Some results: real estate returns are driven by fundamental macroeconomic factors such as the growth rate in real per capita consumption, the real T-bill rate, the term structure of interest rates, and unexpected inflation (Naranjo & Ling, Citation1997); exchange‐traded real estate is integrated with the market for exchange‐traded non‐real estate stocks and the growth rate in real per capita consumption is a common variable (Ling & Naranjo, Citation1999); fundamental and nonfundamental factors such as debt capital market conditions, unemployment, NAREIT and NCREIF returns, stock market volatility, and investor sentiment are significant predictors of ex ante risk premiums (Beracha et al., Citation2019); cross-sectional dispersion of real estate returns is explained by macroeconomic factors such as the term and credit spreads, inflation, and the short rate of interest (Plazzi et al., Citation2008); and REITs with properties in high-density locations have lower implied capitalization rates (Fisher et al., Citation2020).6 The estimated model relates the excess cap rate to the spread between the current BAA bond rate and the current three-month T-bill rate, the one-period lagged BAA bond rate and the one-period lagged three-month T-bill rate, the two-period lagged BAA bond rate and the two-period lagged three-month T-bill rate, the current S&P 500 stock market return rate the current three-month T-bill rate, the one-period lagged S&P 500 stock market return rate and the one-period lagged three-month T-bill rate, the two-period lagged S&P 500 stock market return rate and the two-period lagged three-month T-bill rate. The effect of growth is assumed to be captured by fixed effects MSA variables.7 The Gordon model, rearranged to solve for the cost of equity capital, is based on a dividend (D1) received starting in period 1, a current stock price of P0, and a dividend stream growing at a constant growth rate g into the foreseeable future. The cost of equity capital using this model is rE=D1P0+g.8 For a detailed explanation of this approach from a financial management perspective, see Emery et al. (Citation2018, pp. 132–134).9 Most properties with gross leases were also ground lease transactions. A small percentage of gross leases occurred in fee simple ownership. In the interest of obtaining reliable regression estimates, these observations were deleted from the sample.10 As Letdin et al. (Citation2023) pointed out, this positive relationship is likely due to additional maintenance costs as building size increases.11 Cap rates vary widely depending on the strength of the operator and with a franchise, the cash flow (to cover rents) is riskier than a dealer-operated property. Investors might see the corporate flag for a property and assume that they are protected, but they are not. Corporate owners represent less risk and, therefore, we would expect franchisee cap rates to be higher.12 Industry Dummies include: Auto, Bank, Cellular, Education, Fitness, Gas Station, Government, Grocery, Industrial, Large Retail, Medical, Multi, Office, Pharmacy, Restaurant, and Small Retail. Deal Type Dummies include: Fee Simple, Ground Lease, and Leasehold. Lease Type Dummies include: GL, N, NN, and NNN. Ownership Type Dummies include: Corporate, Franchisee, and Other.13 As shown in Appendix A, we ran our baseline regression using the BBB corporate bond spread (in place of the AAA spread), consistent with Jud and Winkler (Citation1995), and found weaker statistical significance, but similar economic interpretations. For instance, the R2 of column 1 in Appendix A is 0.363, which is much lower than the R2 of 0.610 in column 4 of Table 2, Panel A. This suggests that there is substantial corporate default risk information in BBB spreads that is not found in real estate cap rates.14 Jud and Winkler (Citation1995) used dummy variables to capture differences in MSA characteristics.15 These variables are potentially endogenous, as they may be based on some of the same real estate transactions as in our dataset.16 It is important to note that these three variables exhibit strong correlations. Relative to Housing Supply Elasticity, the Wharton Land Regulation Index and Land Share Unavailable for Development exhibit correlations of –55% and –76%, respectively, for MSAs in our sample. Furthermore, the first principal component explains 67% of the variation among the three variables. Despite the correlations, however, when all three are included in regression, the R2 increases to 0.38 (unreported result).17 Because the data appear to be error free (not mistyped, misreported, etc.), the observations beyond the 5th and 95th percentile are deemed to be legitimate data that should be included in the statistical analysis. Therefore, we report the findings based on the untransformed data.","PeriodicalId":51567,"journal":{"name":"Journal of Real Estate Research","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Effect of Market Asset Returns, Economic Conditions, and Firm Fundamentals on Net Lease Capitalization Rates\",\"authors\":\"Stace Sirmans, Stacy Sirmans, Greg Smersh, Daniel Winkler\",\"doi\":\"10.1080/08965803.2023.2266282\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AbstractThis study fills a void in the literature by examining real estate capitalization rates for single-tenant net lease (STNL) properties. First, we examine cap rate variation in relation to market and firm-level fundamentals using individual transaction data in a multistage regression approach. Second, our single-tenant dataset, which allows us to control for characteristics such as industry and tenant credit ratings, gives us unique insight into not only the pricing of cap rates, but also their underlying drivers and their relationship to market fundamentals and returns on alternative assets. Using this unique dataset of more than 8,000 single-tenant net lease retail property transactions, we develop a quarterly cap rate index controlling for Metropolitan Statistical Area (MSA) and industry fixed effects, property and lease characteristics, and localized influences such as population density and household income. Third, we examine the effect of excess corporate bond spreads, excess stock returns, stock market indicators, firm financials, and economic and demographic indicators. Finally, we examine the effect on cap rates of MSA characteristics such as size, wealth, poverty, crime, gross domestic product, and growth. The findings show that, besides the systematic risk from stock and bond returns, national and metropolitan economic forces and firm fundamental factors explain variation in cap rates.Keywords: cap ratesnet leaseexcess returnmacroeconomic factors AcknowledgmentsWe thank the editor and the anonymous referees for their insightful feedback, which has substantially improved this article. We also thank the participants at the 2023 ARES Conference for their helpful comments.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 As a percentage of total investible wealth, real estate is a significant percentage of domestic publicly traded equities. According to Siblis Research Ltd, the total market capitalization of the U.S. stock market was $53.4 trillion as of December 31, 2021 (https://siblisresearch.com/data/us-stock-market-value/) while, according to the National Association of Real Estate Investment Trusts (NAREIT), the total value of U.S. commercial real estate was $20.7 trillion as of June 2021 (https://www.reit.com/data-research/research/nareit-research/estimating-size-commercial-real-estate-market-us-2021).2 The cap rate is defined as the ratio of a property’s net operating income to its current market value; most often the cap rate is based on expected net operating income.3 Common multifactor models in the finance literature include the Fama-French three-factor model (1992) and the arbitrage pricing theory (APT) model (Ross, Citation1976).4 All the studies discussed in this section use either the full cap rate or the excess cap rate (typically defined as the full cap rate minus the three-month TB yield) as the dependent variable in their analysis.5 To examine the relationship between real estate and capital markets, additional studies have used alternative return measures such as publicly traded REITs or National Council of Real Estate Investment Fiduciaries (NCREIF) returns data. Some results: real estate returns are driven by fundamental macroeconomic factors such as the growth rate in real per capita consumption, the real T-bill rate, the term structure of interest rates, and unexpected inflation (Naranjo & Ling, Citation1997); exchange‐traded real estate is integrated with the market for exchange‐traded non‐real estate stocks and the growth rate in real per capita consumption is a common variable (Ling & Naranjo, Citation1999); fundamental and nonfundamental factors such as debt capital market conditions, unemployment, NAREIT and NCREIF returns, stock market volatility, and investor sentiment are significant predictors of ex ante risk premiums (Beracha et al., Citation2019); cross-sectional dispersion of real estate returns is explained by macroeconomic factors such as the term and credit spreads, inflation, and the short rate of interest (Plazzi et al., Citation2008); and REITs with properties in high-density locations have lower implied capitalization rates (Fisher et al., Citation2020).6 The estimated model relates the excess cap rate to the spread between the current BAA bond rate and the current three-month T-bill rate, the one-period lagged BAA bond rate and the one-period lagged three-month T-bill rate, the two-period lagged BAA bond rate and the two-period lagged three-month T-bill rate, the current S&P 500 stock market return rate the current three-month T-bill rate, the one-period lagged S&P 500 stock market return rate and the one-period lagged three-month T-bill rate, the two-period lagged S&P 500 stock market return rate and the two-period lagged three-month T-bill rate. The effect of growth is assumed to be captured by fixed effects MSA variables.7 The Gordon model, rearranged to solve for the cost of equity capital, is based on a dividend (D1) received starting in period 1, a current stock price of P0, and a dividend stream growing at a constant growth rate g into the foreseeable future. The cost of equity capital using this model is rE=D1P0+g.8 For a detailed explanation of this approach from a financial management perspective, see Emery et al. (Citation2018, pp. 132–134).9 Most properties with gross leases were also ground lease transactions. A small percentage of gross leases occurred in fee simple ownership. In the interest of obtaining reliable regression estimates, these observations were deleted from the sample.10 As Letdin et al. (Citation2023) pointed out, this positive relationship is likely due to additional maintenance costs as building size increases.11 Cap rates vary widely depending on the strength of the operator and with a franchise, the cash flow (to cover rents) is riskier than a dealer-operated property. Investors might see the corporate flag for a property and assume that they are protected, but they are not. Corporate owners represent less risk and, therefore, we would expect franchisee cap rates to be higher.12 Industry Dummies include: Auto, Bank, Cellular, Education, Fitness, Gas Station, Government, Grocery, Industrial, Large Retail, Medical, Multi, Office, Pharmacy, Restaurant, and Small Retail. Deal Type Dummies include: Fee Simple, Ground Lease, and Leasehold. Lease Type Dummies include: GL, N, NN, and NNN. 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Relative to Housing Supply Elasticity, the Wharton Land Regulation Index and Land Share Unavailable for Development exhibit correlations of –55% and –76%, respectively, for MSAs in our sample. Furthermore, the first principal component explains 67% of the variation among the three variables. Despite the correlations, however, when all three are included in regression, the R2 increases to 0.38 (unreported result).17 Because the data appear to be error free (not mistyped, misreported, etc.), the observations beyond the 5th and 95th percentile are deemed to be legitimate data that should be included in the statistical analysis. 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引用次数: 0

摘要

摘要本研究通过考察单租户净租赁(STNL)物业的房地产资本化率,填补了文献中的空白。首先,我们在多阶段回归方法中使用个人交易数据检查上限率变化与市场和公司层面基本面的关系。其次,我们的单租户数据集允许我们控制行业和租户信用评级等特征,使我们不仅能够独特地了解上限费率的定价,还可以了解其潜在驱动因素及其与市场基本面和替代资产回报的关系。利用这个独特的数据集,超过8000单租户净租赁零售物业交易,我们开发了一个季度上限率指数,控制大都市统计地区(MSA)和行业固定效应,物业和租赁特征,以及人口密度和家庭收入等局部影响。第三,我们考察了超额公司债券利差、超额股票回报、股票市场指标、公司财务以及经济和人口指标的影响。最后,我们研究了MSA特征(如规模、财富、贫困、犯罪、国内生产总值和增长)对上限率的影响。研究结果表明,除了股票和债券回报的系统性风险外,国家和城市的经济力量以及坚定的基本面因素也解释了上限利率的变化。关键词:封顶率净租金超额收益宏观经济因素致谢感谢编者和匿名审稿人的深刻反馈,他们的意见大大改进了本文。我们也感谢2023年ARES会议与会者提供的有益意见。披露声明作者未报告潜在的利益冲突。注1作为可投资财富总额的一个百分比,房地产在国内公开交易的股票中所占的比例很大。根据Siblis Research Ltd的数据,截至2021年12月31日,美国股市的总市值为53.4万亿美元(https://siblisresearch.com/data/us-stock-market-value/),而根据全国房地产投资信托协会(NAREIT)的数据,截至2021年6月,美国商业房地产的总价值为20.7万亿美元(https://www.reit.com/data-research/research/nareit-research/estimating-size-commercial-real-estate-market-us-2021).2上限率的定义是房地产的净营业收入与其当前市场价值的比率;大多数情况下,上限费率是基于预期的净营业收入金融文献中常见的多因素模型包括Fama-French三因素模型(1992)和套利定价理论(APT)模型(Ross, Citation1976)本节讨论的所有研究都使用全额封顶率或超额封顶率(通常定义为全额封顶率减去三个月TB收益率)作为其分析中的因变量为了检验房地产和资本市场之间的关系,其他研究使用了其他回报措施,如公开交易的REITs或全国房地产投资受托人委员会(NCREIF)的回报数据。一些结果:房地产收益受基本宏观经济因素驱动,如实际人均消费增长率、实际国库券利率、利率期限结构和意外通货膨胀(Naranjo & Ling, Citation1997);交易所交易的房地产与交易所交易的非房地产股票市场相结合,实际人均消费的增长率是一个共同的变量(Ling & Naranjo, Citation1999);基本和非基本因素,如债务资本市场状况、失业率、NAREIT和NCREIF回报、股市波动和投资者情绪,是事前风险溢价的重要预测因素(Beracha等人,Citation2019);房地产收益的横截面分散由宏观经济因素解释,如期限和信贷利差、通货膨胀和短期利率(Plazzi等人,Citation2008);高密度地区的房地产投资信托基金的隐含资本化率较低(Fisher等人,Citation2020)估计模型将超额上限率与当前BAA债券利率与当前三个月国库券利率之差、滞后一期BAA债券利率与滞后一期3个月国库券利率之差、滞后两期BAA债券利率与滞后两期3个月国库券利率之差、当前标普500股票市场收益率与当前3个月国库券利率之差、滞后一期标普500股票市场收益率与滞后一期3个月国库券利率之差、滞后一期标普500股票市场收益率与滞后一期3个月国库券利率之差联系起来。滞后于标准普尔500指数的两期股市回报率和滞后于三个月期美国国债的两期收益率。假设增长的影响是由固定效应的MSA变量捕获的。 Gordon模型,重新安排以解决权益资本成本,是基于从第1时期开始收到的股息(D1),当前股票价格为P0,股息流以恒定的增长率g增长到可预见的未来。使用该模型的权益资本成本为rE=D1P0+g.8有关从财务管理角度对这种方法的详细解释,请参见Emery等人(Citation2018, pp. 132-134)大部分物业的总租约亦属土地租约交易。总租赁中有一小部分是单纯所有权。为了获得可靠的回归估计,从样本中删除了这些观测值正如Letdin等人(Citation2023)指出的那样,这种积极的关系可能是由于随着建筑规模的增加而增加的额外维护成本上限费率因经营者的实力而有很大差异,而特许经营的现金流(用于支付租金)比经销商经营的物业风险更大。投资者可能会看到一处房产的公司标志,并认为他们受到了保护,但事实并非如此。公司所有者代表的风险较小,因此,我们预计加盟商的上限率会更高工业假人包括:汽车,银行,手机,教育,健身,加油站,政府,食品杂货,工业,大型零售,医疗,多,办公室,药房,餐厅和小型零售。交易类型假人包括:简单费用,土地租赁和租赁。租赁类型包括:GL、N、NN、NNN。如附录A所示,我们使用BBB级公司债券利差(代替AAA级利差)进行基线回归,与Jud和Winkler (Citation1995)一致,发现统计显著性较弱,但经济解释相似。例如,附录A中第一列的R2为0.363,远低于表2 Panel A中第四列的R2为0.610,这表明BBB价差中存在大量房地产上限利率中没有的企业违约风险信息Jud和Winkler (Citation1995)使用虚拟变量来捕捉MSA特征的差异这些变量可能是内生的,因为它们可能基于我们数据集中的一些相同的房地产交易值得注意的是,这三个变量表现出很强的相关性。相对于住房供应弹性,沃顿土地调控指数和不可用于发展的土地份额在我们的样本中分别显示出-55%和-76%的相关性。此外,第一主成分解释了三个变量之间67%的差异。尽管存在相关性,但是,当所有三者都包括在回归中时,R2增加到0.38(未报告的结果)因为数据看起来没有错误(没有输入错误、误报等),所以超过第5和第95百分位的观测值被认为是合法的数据,应该包括在统计分析中。因此,我们报告的发现基于未转换的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Effect of Market Asset Returns, Economic Conditions, and Firm Fundamentals on Net Lease Capitalization Rates
AbstractThis study fills a void in the literature by examining real estate capitalization rates for single-tenant net lease (STNL) properties. First, we examine cap rate variation in relation to market and firm-level fundamentals using individual transaction data in a multistage regression approach. Second, our single-tenant dataset, which allows us to control for characteristics such as industry and tenant credit ratings, gives us unique insight into not only the pricing of cap rates, but also their underlying drivers and their relationship to market fundamentals and returns on alternative assets. Using this unique dataset of more than 8,000 single-tenant net lease retail property transactions, we develop a quarterly cap rate index controlling for Metropolitan Statistical Area (MSA) and industry fixed effects, property and lease characteristics, and localized influences such as population density and household income. Third, we examine the effect of excess corporate bond spreads, excess stock returns, stock market indicators, firm financials, and economic and demographic indicators. Finally, we examine the effect on cap rates of MSA characteristics such as size, wealth, poverty, crime, gross domestic product, and growth. The findings show that, besides the systematic risk from stock and bond returns, national and metropolitan economic forces and firm fundamental factors explain variation in cap rates.Keywords: cap ratesnet leaseexcess returnmacroeconomic factors AcknowledgmentsWe thank the editor and the anonymous referees for their insightful feedback, which has substantially improved this article. We also thank the participants at the 2023 ARES Conference for their helpful comments.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 As a percentage of total investible wealth, real estate is a significant percentage of domestic publicly traded equities. According to Siblis Research Ltd, the total market capitalization of the U.S. stock market was $53.4 trillion as of December 31, 2021 (https://siblisresearch.com/data/us-stock-market-value/) while, according to the National Association of Real Estate Investment Trusts (NAREIT), the total value of U.S. commercial real estate was $20.7 trillion as of June 2021 (https://www.reit.com/data-research/research/nareit-research/estimating-size-commercial-real-estate-market-us-2021).2 The cap rate is defined as the ratio of a property’s net operating income to its current market value; most often the cap rate is based on expected net operating income.3 Common multifactor models in the finance literature include the Fama-French three-factor model (1992) and the arbitrage pricing theory (APT) model (Ross, Citation1976).4 All the studies discussed in this section use either the full cap rate or the excess cap rate (typically defined as the full cap rate minus the three-month TB yield) as the dependent variable in their analysis.5 To examine the relationship between real estate and capital markets, additional studies have used alternative return measures such as publicly traded REITs or National Council of Real Estate Investment Fiduciaries (NCREIF) returns data. Some results: real estate returns are driven by fundamental macroeconomic factors such as the growth rate in real per capita consumption, the real T-bill rate, the term structure of interest rates, and unexpected inflation (Naranjo & Ling, Citation1997); exchange‐traded real estate is integrated with the market for exchange‐traded non‐real estate stocks and the growth rate in real per capita consumption is a common variable (Ling & Naranjo, Citation1999); fundamental and nonfundamental factors such as debt capital market conditions, unemployment, NAREIT and NCREIF returns, stock market volatility, and investor sentiment are significant predictors of ex ante risk premiums (Beracha et al., Citation2019); cross-sectional dispersion of real estate returns is explained by macroeconomic factors such as the term and credit spreads, inflation, and the short rate of interest (Plazzi et al., Citation2008); and REITs with properties in high-density locations have lower implied capitalization rates (Fisher et al., Citation2020).6 The estimated model relates the excess cap rate to the spread between the current BAA bond rate and the current three-month T-bill rate, the one-period lagged BAA bond rate and the one-period lagged three-month T-bill rate, the two-period lagged BAA bond rate and the two-period lagged three-month T-bill rate, the current S&P 500 stock market return rate the current three-month T-bill rate, the one-period lagged S&P 500 stock market return rate and the one-period lagged three-month T-bill rate, the two-period lagged S&P 500 stock market return rate and the two-period lagged three-month T-bill rate. The effect of growth is assumed to be captured by fixed effects MSA variables.7 The Gordon model, rearranged to solve for the cost of equity capital, is based on a dividend (D1) received starting in period 1, a current stock price of P0, and a dividend stream growing at a constant growth rate g into the foreseeable future. The cost of equity capital using this model is rE=D1P0+g.8 For a detailed explanation of this approach from a financial management perspective, see Emery et al. (Citation2018, pp. 132–134).9 Most properties with gross leases were also ground lease transactions. A small percentage of gross leases occurred in fee simple ownership. In the interest of obtaining reliable regression estimates, these observations were deleted from the sample.10 As Letdin et al. (Citation2023) pointed out, this positive relationship is likely due to additional maintenance costs as building size increases.11 Cap rates vary widely depending on the strength of the operator and with a franchise, the cash flow (to cover rents) is riskier than a dealer-operated property. Investors might see the corporate flag for a property and assume that they are protected, but they are not. Corporate owners represent less risk and, therefore, we would expect franchisee cap rates to be higher.12 Industry Dummies include: Auto, Bank, Cellular, Education, Fitness, Gas Station, Government, Grocery, Industrial, Large Retail, Medical, Multi, Office, Pharmacy, Restaurant, and Small Retail. Deal Type Dummies include: Fee Simple, Ground Lease, and Leasehold. Lease Type Dummies include: GL, N, NN, and NNN. Ownership Type Dummies include: Corporate, Franchisee, and Other.13 As shown in Appendix A, we ran our baseline regression using the BBB corporate bond spread (in place of the AAA spread), consistent with Jud and Winkler (Citation1995), and found weaker statistical significance, but similar economic interpretations. For instance, the R2 of column 1 in Appendix A is 0.363, which is much lower than the R2 of 0.610 in column 4 of Table 2, Panel A. This suggests that there is substantial corporate default risk information in BBB spreads that is not found in real estate cap rates.14 Jud and Winkler (Citation1995) used dummy variables to capture differences in MSA characteristics.15 These variables are potentially endogenous, as they may be based on some of the same real estate transactions as in our dataset.16 It is important to note that these three variables exhibit strong correlations. Relative to Housing Supply Elasticity, the Wharton Land Regulation Index and Land Share Unavailable for Development exhibit correlations of –55% and –76%, respectively, for MSAs in our sample. Furthermore, the first principal component explains 67% of the variation among the three variables. Despite the correlations, however, when all three are included in regression, the R2 increases to 0.38 (unreported result).17 Because the data appear to be error free (not mistyped, misreported, etc.), the observations beyond the 5th and 95th percentile are deemed to be legitimate data that should be included in the statistical analysis. Therefore, we report the findings based on the untransformed data.
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来源期刊
CiteScore
1.40
自引率
12.50%
发文量
29
期刊介绍: The American Real Estate Society (ARES), founded in 1985, is an association of real estate thought leaders. Members are drawn from academia and the profession at large, both in the United States and internationally. The Society is dedicated to producing and disseminating knowledge related to real estate decision making and the functioning of real estate markets. The objectives of the American Real Estate Society are to encourage research and promote education in real estate, improve communication and exchange of information in real estate and allied matters among college/university faculty and practicing professionals, and facilitate the association of academic, practicing professional, and research persons in the area of real estate.
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