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The cap rate series used are from Green Street Advisors and represent transaction cap rates which avoids the problem of artificial smoothness found in appraisal-based cap rates.FindingsUsing a VECM specified with the novel variable, unemployment and past cap rates contains enough information to produce more robust forecasts than the traditional variables (return expectations and risk premiums). The method is robust both in and out of sample.Practical implicationsThis has direct implications for governmental policy, offering a path to real estate price stability and growth through mortgage access–functions largely influenced by the Fed and the quasi-federal agencies Fannie Mae and Freddie Mac. It also offers a timely alternative to interest rate-based forecasting models, which are likely to be less useful as interest rates are to be held low for the foreseeable future.Originality/valueThis study offers a new and highly explanatory variable to the literature while being among the only to model either (1) transactional cap rates (versus appraisal) (2) out-of-sample data (versus in-sample) (3) without the use of the traditional variables thought to be integral to cap rate modelling (return expectations and risk premiums).","PeriodicalId":46429,"journal":{"name":"Journal of Property Investment & Finance","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The determinants of capitalization rates: evidence from the US real estate markets\",\"authors\":\"M. Larriva, Peter D. 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引用次数: 2
摘要
目的建立一个新的变量的强度-抵押贷款债务占美国国内生产总值(GDP)的一部分-预测美国办公室和多家庭部门的资本化率。设计/方法/方法作者为数据指定了一个矢量误差校正模型(VECM)。VECM用于解决金融变量的非平稳性问题,同时保持嵌入在数据水平中的信息,而不是它们的差异。所使用的封顶率系列来自Green Street Advisors,代表交易封顶率,从而避免了基于评估的封顶率中存在的人为平滑问题。使用带有新变量的VECM,失业率和过去的上限率包含足够的信息来产生比传统变量(回报预期和风险溢价)更稳健的预测。该方法在样本内外均具有较强的鲁棒性。实际意义这对政府政策有直接的影响,通过抵押贷款获取功能(很大程度上受美联储和准联邦机构房利美和房地美影响)为房地产价格稳定和增长提供了一条途径。它还提供了一个及时的替代基于利率的预测模型,由于利率在可预见的未来将保持在低位,这种预测模型可能用处不大。原创性/价值本研究为文献提供了一个新的和高度解释性的变量,同时是唯一的模型之一(1)交易上限率(相对于评估)(2)样本外数据(相对于样本内)(3)没有使用被认为是上限率建模不可或缺的传统变量(回报预期和风险溢价)。
The determinants of capitalization rates: evidence from the US real estate markets
PurposeEstablishing the strength of a novel variable–mortgage debt as a fraction of US gross domestic product (GDP)–on forecasting capitalization rates in both the US office and multifamily sectors.Design/methodology/approachThe authors specifies a vector error correction model (VECM) to the data. VECM are used to address the nonstationarity issues of financial variables while maintaining the information embedded in the levels of the data, as opposed to their differences. The cap rate series used are from Green Street Advisors and represent transaction cap rates which avoids the problem of artificial smoothness found in appraisal-based cap rates.FindingsUsing a VECM specified with the novel variable, unemployment and past cap rates contains enough information to produce more robust forecasts than the traditional variables (return expectations and risk premiums). The method is robust both in and out of sample.Practical implicationsThis has direct implications for governmental policy, offering a path to real estate price stability and growth through mortgage access–functions largely influenced by the Fed and the quasi-federal agencies Fannie Mae and Freddie Mac. It also offers a timely alternative to interest rate-based forecasting models, which are likely to be less useful as interest rates are to be held low for the foreseeable future.Originality/valueThis study offers a new and highly explanatory variable to the literature while being among the only to model either (1) transactional cap rates (versus appraisal) (2) out-of-sample data (versus in-sample) (3) without the use of the traditional variables thought to be integral to cap rate modelling (return expectations and risk premiums).
期刊介绍:
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