Pub Date : 2021-04-03DOI: 10.1080/09599916.2021.1917885
Rainer Schulz, Martin Wersing
In 2019, as guest editors of the Journal of Property Research, we called for contributions to the special issue Automated Valuation Services (AVSs). We were interested in particular in case studies that discuss the development, implementation, and operation of an AVS. We are very grateful to Bryan MacGregor, the editor of the journal, and to the many reviewers, who assessed the submissions and helped us with the selection of the four papers that have been included in the special issue. While there are already many papers that examine the performance of different statistical models for market value predictions of residential properties, only a few papers examine how to implement such models as a service for users on an ongoing basis. Users expect that such a service is easy to use, and they also expect that it is timely and robust. A service should provide a prediction of the market value, but should also indicate the uncertainty of this prediction in a manner that the user can understand. Methods from machine learning are increasingly used for these tasks and it can be difficult to explain these methods to non-experts. If it is important that details on the methods should be communicated to users, then this should be done as clearly as possible. The first paper by Hill et al. (2021) examines the importance of the performance measure used for the selection of the statistical model for an AVS. As there are usually competing statistical models, each should be fitted to transaction data with a rolling windows approach. Given the market value predictions from each of the competing models, sets of out-of-sample prediction errors can be computed. Obviously, the model with the ‘best’ prediction errors should be chosen. This requires, however, that each set of prediction errors is aggregated into measures that can be compared. Hill et al. (2021) provide a review and analysis of performance measures that have been proposed in the literature. Their classification of performance measures – and transformations of these – with respect to different aspects of the distribution of prediction errors underscores the necessity to align model selection with the application at hand. The authors examine this empirically with data from flat transactions from Graz, Austria. Based on their analysis, Hill et al. (2021) recommend seven core measures, each addressing a different aspect of the ‘best’ model. The second paper by Krause et al. (2020) addresses that every market value prediction – by its very nature – has an inherent uncertainty to it. The statistical model used in an AVS can provide estimates of uncertainty, such as prediction intervals, with ease and high accuracy. The authors start by unifying the terminology with which to discuss uncertainty. This is useful given the varied terminology in academic research
{"title":"Introduction to special issue","authors":"Rainer Schulz, Martin Wersing","doi":"10.1080/09599916.2021.1917885","DOIUrl":"https://doi.org/10.1080/09599916.2021.1917885","url":null,"abstract":"In 2019, as guest editors of the Journal of Property Research, we called for contributions to the special issue Automated Valuation Services (AVSs). We were interested in particular in case studies that discuss the development, implementation, and operation of an AVS. We are very grateful to Bryan MacGregor, the editor of the journal, and to the many reviewers, who assessed the submissions and helped us with the selection of the four papers that have been included in the special issue. While there are already many papers that examine the performance of different statistical models for market value predictions of residential properties, only a few papers examine how to implement such models as a service for users on an ongoing basis. Users expect that such a service is easy to use, and they also expect that it is timely and robust. A service should provide a prediction of the market value, but should also indicate the uncertainty of this prediction in a manner that the user can understand. Methods from machine learning are increasingly used for these tasks and it can be difficult to explain these methods to non-experts. If it is important that details on the methods should be communicated to users, then this should be done as clearly as possible. The first paper by Hill et al. (2021) examines the importance of the performance measure used for the selection of the statistical model for an AVS. As there are usually competing statistical models, each should be fitted to transaction data with a rolling windows approach. Given the market value predictions from each of the competing models, sets of out-of-sample prediction errors can be computed. Obviously, the model with the ‘best’ prediction errors should be chosen. This requires, however, that each set of prediction errors is aggregated into measures that can be compared. Hill et al. (2021) provide a review and analysis of performance measures that have been proposed in the literature. Their classification of performance measures – and transformations of these – with respect to different aspects of the distribution of prediction errors underscores the necessity to align model selection with the application at hand. The authors examine this empirically with data from flat transactions from Graz, Austria. Based on their analysis, Hill et al. (2021) recommend seven core measures, each addressing a different aspect of the ‘best’ model. The second paper by Krause et al. (2020) addresses that every market value prediction – by its very nature – has an inherent uncertainty to it. The statistical model used in an AVS can provide estimates of uncertainty, such as prediction intervals, with ease and high accuracy. The authors start by unifying the terminology with which to discuss uncertainty. This is useful given the varied terminology in academic research","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":"38 1","pages":"(ii) - (iv)"},"PeriodicalIF":1.9,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2021.1917885","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45439575","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}
Pub Date : 2021-04-03DOI: 10.1080/09599916.2020.1858937
Miriam Steurer, R. Hill, Norbert Pfeifer
ABSTRACT Automated Valuation Models (AVMs) based on Machine Learning (ML) algorithms are widely used for predicting house prices. While there is consensus in the literature that cross-validation (CV) should be used for model selection in this context, the interdisciplinary nature of the subject has made it hard to reach consensus over which metrics to use at each stage of the CV exercise. We collect 48 metrics (from the AVM literature and elsewhere) and classify them into seven groups according to their structure. Each of these groups focuses on a particular aspect of the error distribution. Depending on the type of data and the purpose of the AVM, the needs of users may be met by some classes, but not by others. In addition, we show in an empirical application how the choice of metric can influence the choice of model, by applying each metric to evaluate five commonly used AVM models. Finally – since it is not always practicable to produce 48 different performance metrics – we provide a short list of 7 metrics that are well suited to evaluate AVMs. These metrics satisfy a symmetry condition that we find is important for AVM performance, and can provide a good overall model performance ranking.
{"title":"Metrics for evaluating the performance of machine learning based automated valuation models","authors":"Miriam Steurer, R. Hill, Norbert Pfeifer","doi":"10.1080/09599916.2020.1858937","DOIUrl":"https://doi.org/10.1080/09599916.2020.1858937","url":null,"abstract":"ABSTRACT Automated Valuation Models (AVMs) based on Machine Learning (ML) algorithms are widely used for predicting house prices. While there is consensus in the literature that cross-validation (CV) should be used for model selection in this context, the interdisciplinary nature of the subject has made it hard to reach consensus over which metrics to use at each stage of the CV exercise. We collect 48 metrics (from the AVM literature and elsewhere) and classify them into seven groups according to their structure. Each of these groups focuses on a particular aspect of the error distribution. Depending on the type of data and the purpose of the AVM, the needs of users may be met by some classes, but not by others. In addition, we show in an empirical application how the choice of metric can influence the choice of model, by applying each metric to evaluate five commonly used AVM models. Finally – since it is not always practicable to produce 48 different performance metrics – we provide a short list of 7 metrics that are well suited to evaluate AVMs. These metrics satisfy a symmetry condition that we find is important for AVM performance, and can provide a good overall model performance ranking.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":"38 1","pages":"99 - 129"},"PeriodicalIF":1.9,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2020.1858937","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46043784","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}
Pub Date : 2021-04-03DOI: 10.1080/09599916.2021.1905690
Nils Hinrichs, Jens Kolbe, A. Werwatz
ABSTRACT We study whether data-driven AVM specification that combines a flexible-yet-simple regression model with shrinkage estimators considerably improves upon the prediction accuracy of a conventional hedonic model. A rolling window prediction comparison based on all condominium sales in Berlin, Germany, between 1996 and 2013 delivered the following results. The highly parameterised model can result in extreme errors if the flexible model, which employs roughly 3,800 variables, is estimated by OLS and even if shrinkage is applied via Ridge regression. Once the most extreme errors are disregarded, Ridge regression appears as the clear winner of the prediction comparison. It is the only procedure that delivers a considerable reduction in the root mean squared prediction error relative to a parsimonious benchmark model (estimated via OLS). Of the two procedures with variable selection capability, Elastic Net delivers a slightly better prediction performance. Lasso, on the other hand, acts considerably more as a selector and typically sets the bulk of the several thousand coefficients to zero. Both procedures largely agree in terms of which characteristics they frequently select: core characteristics of hedonic pricing such as floor space, building age and location dummies.
{"title":"Using shrinkage for data-driven automated valuation model specification – a case study from Berlin","authors":"Nils Hinrichs, Jens Kolbe, A. Werwatz","doi":"10.1080/09599916.2021.1905690","DOIUrl":"https://doi.org/10.1080/09599916.2021.1905690","url":null,"abstract":"ABSTRACT We study whether data-driven AVM specification that combines a flexible-yet-simple regression model with shrinkage estimators considerably improves upon the prediction accuracy of a conventional hedonic model. A rolling window prediction comparison based on all condominium sales in Berlin, Germany, between 1996 and 2013 delivered the following results. The highly parameterised model can result in extreme errors if the flexible model, which employs roughly 3,800 variables, is estimated by OLS and even if shrinkage is applied via Ridge regression. Once the most extreme errors are disregarded, Ridge regression appears as the clear winner of the prediction comparison. It is the only procedure that delivers a considerable reduction in the root mean squared prediction error relative to a parsimonious benchmark model (estimated via OLS). Of the two procedures with variable selection capability, Elastic Net delivers a slightly better prediction performance. Lasso, on the other hand, acts considerably more as a selector and typically sets the bulk of the several thousand coefficients to zero. Both procedures largely agree in terms of which characteristics they frequently select: core characteristics of hedonic pricing such as floor space, building age and location dummies.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":"38 1","pages":"130 - 153"},"PeriodicalIF":1.9,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2021.1905690","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48455812","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}
Pub Date : 2021-04-03DOI: 10.1080/09599916.2021.1926053
Anonymous
Article title: Uncertainty in automated valuation models: Error-based versus modelbased approaches. Authors: Andy Krause, Andrew Martin & Matthew Fix. Journal: Journal of Property Research Bibliometrics: Volume 37, Number 4, pages 308-339. DOI: https://doi.org/10.1080/09599916.2020.1807587 The article mentioned above is part of a group of papers with a special theme entitled “Automated Valuation Services” and should have been published in issue 37(4). Taylor & Francis apologises for this error. JOURNAL OF PROPERTY RESEARCH 2021, VOL. 38, NO. 2, 174 https://doi.org/10.1080/09599916.2021.1926053
文章标题:自动估价模型中的不确定性:基于错误的方法与基于模型的方法。作者:安迪·克劳斯，安德鲁·马丁和马修·费克斯。期刊:Journal of Property Research .文献计量学:第37卷，第4期，308-339页。上述文章是题为“自动估价服务”的专题论文组的一部分，本应在第37期(4)中发表。Taylor & Francis为这个错误道歉。房地产研究，2021,vol . 38, no . 1。2,174 https://doi.org/10.1080/09599916.2021.1926053
{"title":"Correction","authors":"Anonymous","doi":"10.1080/09599916.2021.1926053","DOIUrl":"https://doi.org/10.1080/09599916.2021.1926053","url":null,"abstract":"Article title: Uncertainty in automated valuation models: Error-based versus modelbased approaches. Authors: Andy Krause, Andrew Martin & Matthew Fix. Journal: Journal of Property Research Bibliometrics: Volume 37, Number 4, pages 308-339. DOI: https://doi.org/10.1080/09599916.2020.1807587 The article mentioned above is part of a group of papers with a special theme entitled “Automated Valuation Services” and should have been published in issue 37(4). Taylor & Francis apologises for this error. JOURNAL OF PROPERTY RESEARCH 2021, VOL. 38, NO. 2, 174 https://doi.org/10.1080/09599916.2021.1926053","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":"38 1","pages":"174 - 174"},"PeriodicalIF":1.9,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2021.1926053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47619379","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}
Pub Date : 2021-03-29DOI: 10.1080/09599916.2021.1903531
Christian Weis, René-Ojas Woltering, Steffen P. Sebastian
ABSTRACT This paper analyzes the return sensitivities of real estate value and growth stocks to changes in five different interest rate proxies. Using a global sample of 352 listed real estate companies from 12 countries as a test object, we find that real estate value stocks are more sensitive than real estate growth stocks to changes in the short-term interest rate. This finding is consistent with the theory that investors with shorter investment horizons trade off the high initial yield of value stocks against lower-risk short-term interest rates. In contrast, real estate growth stocks are more sensitive to changes in the long-term interest rate, which is consistent with a stronger impact on the present value of the future cash flows of growth stocks. We also find that real estate value stocks are more sensitive to changes in the credit yield. Because credit costs have a direct impact on a firm’s cost of capital, this result is consistent with risk-based theories of the value premium, which argue value stocks are riskier because they tend to have higher leverage and greater default probability.
{"title":"Which stocks are driven by which interest rates?","authors":"Christian Weis, René-Ojas Woltering, Steffen P. Sebastian","doi":"10.1080/09599916.2021.1903531","DOIUrl":"https://doi.org/10.1080/09599916.2021.1903531","url":null,"abstract":"ABSTRACT This paper analyzes the return sensitivities of real estate value and growth stocks to changes in five different interest rate proxies. Using a global sample of 352 listed real estate companies from 12 countries as a test object, we find that real estate value stocks are more sensitive than real estate growth stocks to changes in the short-term interest rate. This finding is consistent with the theory that investors with shorter investment horizons trade off the high initial yield of value stocks against lower-risk short-term interest rates. In contrast, real estate growth stocks are more sensitive to changes in the long-term interest rate, which is consistent with a stronger impact on the present value of the future cash flows of growth stocks. We also find that real estate value stocks are more sensitive to changes in the credit yield. Because credit costs have a direct impact on a firm’s cost of capital, this result is consistent with risk-based theories of the value premium, which argue value stocks are riskier because they tend to have higher leverage and greater default probability.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":"38 1","pages":"175 - 197"},"PeriodicalIF":1.9,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2021.1903531","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46724890","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}
Pub Date : 2021-03-01DOI: 10.1080/09599916.2020.1861066
Rainer Schulz, Martin Wersing
ABSTRACT Automated valuation services (AVSs) offered by listings platforms predict market values based on property characteristics supplied by users. We investigate the implementation of such a service for the City of Aberdeen. We fit different market value models with machine learning methods and assess them in a rolling windows procedure that mimics an AVS setting. We also investigate the ease and robustness with which the models can be implemented. We discuss how prediction uncertainty can be measured and reported to users. If implemented in the future, such a service has the potential to improve the transparency of the local housing market.
{"title":"Automated Valuation Services: A case study for Aberdeen in Scotland","authors":"Rainer Schulz, Martin Wersing","doi":"10.1080/09599916.2020.1861066","DOIUrl":"https://doi.org/10.1080/09599916.2020.1861066","url":null,"abstract":"ABSTRACT Automated valuation services (AVSs) offered by listings platforms predict market values based on property characteristics supplied by users. We investigate the implementation of such a service for the City of Aberdeen. We fit different market value models with machine learning methods and assess them in a rolling windows procedure that mimics an AVS setting. We also investigate the ease and robustness with which the models can be implemented. We discuss how prediction uncertainty can be measured and reported to users. If implemented in the future, such a service has the potential to improve the transparency of the local housing market.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":"38 1","pages":"154 - 172"},"PeriodicalIF":1.9,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2020.1861066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41939471","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}
Pub Date : 2021-03-01DOI: 10.1080/09599916.2021.1892802
J. Shukla
ABSTRACT Coerciveness built in the process of compulsory acquisition (CA) induces a perception of unfairness among the affected landowners and may alleviate with fair compensation and due process. This research focuses on the latter and aims to identify issues of unfairness perceived by the affected landowners using six criteria of procedural fairness from the legal literature including ethicality, representativeness, bias-suppression, accuracy, correctability and consistency. While procedural unfairness is a concern in many geographies (see for Scotland), this research adopts a case-study approach given the geographical specificity of CA laws and examines Indian process for its relatively recent modification under the newly enacted CA act of 2013. The paper undertakes a qualitative content analysis of court case reports on the Bangalore–Mysore Infrastructure Corridor project and interview transcripts of forty-seven landowners whose land was acquired using Karnataka Industrial Area Development Act of 1966 (a precursor to the new act). This research argues that fixing following issues is crucial to improving the landowners’ perception of fairness: ethical behaviour by the acquirers; representativeness of the affected landowners; quality information throughout the process; accountability of acquirers; neutral review of objections; unbiased assessment of compensation; and inexpensive conflict resolutions.
{"title":"Compulsory yet Fair Acquisition of Land: Assessing Procedural Fairness of Compulsory Acquisition Process in India","authors":"J. Shukla","doi":"10.1080/09599916.2021.1892802","DOIUrl":"https://doi.org/10.1080/09599916.2021.1892802","url":null,"abstract":"ABSTRACT Coerciveness built in the process of compulsory acquisition (CA) induces a perception of unfairness among the affected landowners and may alleviate with fair compensation and due process. This research focuses on the latter and aims to identify issues of unfairness perceived by the affected landowners using six criteria of procedural fairness from the legal literature including ethicality, representativeness, bias-suppression, accuracy, correctability and consistency. While procedural unfairness is a concern in many geographies (see for Scotland), this research adopts a case-study approach given the geographical specificity of CA laws and examines Indian process for its relatively recent modification under the newly enacted CA act of 2013. The paper undertakes a qualitative content analysis of court case reports on the Bangalore–Mysore Infrastructure Corridor project and interview transcripts of forty-seven landowners whose land was acquired using Karnataka Industrial Area Development Act of 1966 (a precursor to the new act). This research argues that fixing following issues is crucial to improving the landowners’ perception of fairness: ethical behaviour by the acquirers; representativeness of the affected landowners; quality information throughout the process; accountability of acquirers; neutral review of objections; unbiased assessment of compensation; and inexpensive conflict resolutions.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":"38 1","pages":"238 - 261"},"PeriodicalIF":1.9,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2021.1892802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45028909","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}
Pub Date : 2021-02-25DOI: 10.1080/09599916.2021.1888774
Dane Bax, T. Zewotir, D. North
ABSTRACT Log linear hedonic models are ubiquitous in econometric real estate research even though functional form assumptions are often not satisfied and the nested structure of homes in suburbs is not captured adequately. This study focuses on appraising different residential property types located throughout South Africa, investigating a flexible approach which does not assume some explicit functional form. The objective of this paper was to fit and compare two hierarchical generalised additive models to 412 500 property listings from 2013 to 2017. A gamma hierarchical model with random intercepts for the suburb provided the best fit and generalisability, while accounting for the spatial dependency in the data. The results show that hierarchical generalised additive models capture complex shapes between listing prices and structural property characteristics, and further reveal that partial pooling is useful to capture between suburb variability.
{"title":"Appraising residential property using hierarchical generalised additive models","authors":"Dane Bax, T. Zewotir, D. North","doi":"10.1080/09599916.2021.1888774","DOIUrl":"https://doi.org/10.1080/09599916.2021.1888774","url":null,"abstract":"ABSTRACT Log linear hedonic models are ubiquitous in econometric real estate research even though functional form assumptions are often not satisfied and the nested structure of homes in suburbs is not captured adequately. This study focuses on appraising different residential property types located throughout South Africa, investigating a flexible approach which does not assume some explicit functional form. The objective of this paper was to fit and compare two hierarchical generalised additive models to 412 500 property listings from 2013 to 2017. A gamma hierarchical model with random intercepts for the suburb provided the best fit and generalisability, while accounting for the spatial dependency in the data. The results show that hierarchical generalised additive models capture complex shapes between listing prices and structural property characteristics, and further reveal that partial pooling is useful to capture between suburb variability.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":"38 1","pages":"198 - 212"},"PeriodicalIF":1.9,"publicationDate":"2021-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2021.1888774","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46665030","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}
Pub Date : 2021-02-13DOI: 10.1080/09599916.2021.1885053
Jan Reinert
ABSTRACT While all valuers are obliged to act impartially and transparently to reduce bias, the closer relationship between valuers and clients among internal valuations may raise additional concerns regarding the independence of the valuer and hence the objectivity of the result. This paper analyses how internal and external valuations differ in their ability to mirror market prices. The dataset for the analyses contained 4,805 commercial properties in Germany between 1995 and 2013. The first part of the analysis was a Market-Adjusted Valuation and Actual Sale Price Comparison, based on sold properties. It showed that a majority of both valuation types had a valuation error within the acceptable threshold of 15% but that external valuations were on average significantly closer to sale prices than internal valuations. Due to sample selection issues, a second analysis, called Actual Valuation and Fitted Sale Price Comparison, was carried out. Real transactions were used to derive hedonic prices that could be compared against valuations of held properties. The Heckman Correction was used to mitigate sample selection bias. The results showed that both valuation types produced a majority of observations within the set threshold but that external valuations were on average closer to sale prices than internal valuations.
{"title":"Valuation accuracy of external and internal property valuations in Germany","authors":"Jan Reinert","doi":"10.1080/09599916.2021.1885053","DOIUrl":"https://doi.org/10.1080/09599916.2021.1885053","url":null,"abstract":"ABSTRACT While all valuers are obliged to act impartially and transparently to reduce bias, the closer relationship between valuers and clients among internal valuations may raise additional concerns regarding the independence of the valuer and hence the objectivity of the result. This paper analyses how internal and external valuations differ in their ability to mirror market prices. The dataset for the analyses contained 4,805 commercial properties in Germany between 1995 and 2013. The first part of the analysis was a Market-Adjusted Valuation and Actual Sale Price Comparison, based on sold properties. It showed that a majority of both valuation types had a valuation error within the acceptable threshold of 15% but that external valuations were on average significantly closer to sale prices than internal valuations. Due to sample selection issues, a second analysis, called Actual Valuation and Fitted Sale Price Comparison, was carried out. Real transactions were used to derive hedonic prices that could be compared against valuations of held properties. The Heckman Correction was used to mitigate sample selection bias. The results showed that both valuation types produced a majority of observations within the set threshold but that external valuations were on average closer to sale prices than internal valuations.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":"38 1","pages":"337 - 354"},"PeriodicalIF":1.9,"publicationDate":"2021-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2021.1885053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43940457","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}
Pub Date : 2021-01-15DOI: 10.1080/09599916.2021.1873405
Yang Yang, Michael Rehm
ABSTRACT Many housing markets across the globe have experienced upward trends in real estate prices during the past two decades. The dynamics between housing prices and speculation have been analysed by existing housing literature, but this study has a few features that may deepen the understanding of this topic. This research uses transaction-level data, focuses on only investor-purchase records, distinguishes leveraged transactions from unleveraged ones and adopts a new proxy for property speculation. Furthermore, the price elasticity of housing supply has been examined as the price responsiveness is important for understanding the topic in a supply-constrained market. We build a stock adjustment model to estimate the elasticity and a vector error-correction model to conduct Granger causality tests, impulse response analyses and a variance decomposition analysis. The findings uncover a feedback loop in a market with inelastic housing supply: investors’ speculative behaviour lifts Auckland housing prices which in turn spur further housing speculation.
{"title":"Housing prices and speculation dynamics: a study of Auckland housing market","authors":"Yang Yang, Michael Rehm","doi":"10.1080/09599916.2021.1873405","DOIUrl":"https://doi.org/10.1080/09599916.2021.1873405","url":null,"abstract":"ABSTRACT Many housing markets across the globe have experienced upward trends in real estate prices during the past two decades. The dynamics between housing prices and speculation have been analysed by existing housing literature, but this study has a few features that may deepen the understanding of this topic. This research uses transaction-level data, focuses on only investor-purchase records, distinguishes leveraged transactions from unleveraged ones and adopts a new proxy for property speculation. Furthermore, the price elasticity of housing supply has been examined as the price responsiveness is important for understanding the topic in a supply-constrained market. We build a stock adjustment model to estimate the elasticity and a vector error-correction model to conduct Granger causality tests, impulse response analyses and a variance decomposition analysis. The findings uncover a feedback loop in a market with inelastic housing supply: investors’ speculative behaviour lifts Auckland housing prices which in turn spur further housing speculation.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":"38 1","pages":"286 - 304"},"PeriodicalIF":1.9,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2021.1873405","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42087590","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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