Pub Date : 2021-01-02DOI: 10.1080/09599916.2020.1832558
Winky K.O. Ho, B. Tang, S. Wong
ABSTRACT This study uses three machine learning algorithms including, support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM) in the appraisal of property prices. It applies these methods to examine a data sample of about 40,000 housing transactions in a period of over 18 years in Hong Kong, and then compares the results of these algorithms. In terms of predictive power, RF and GBM have achieved better performance when compared to SVM. The three performance metrics including mean squared error (MSE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) associated with these two algorithms also unambiguously outperform those of SVM. However, our study has found that SVM is still a useful algorithm in data fitting because it can produce reasonably accurate predictions within a tight time constraint. Our conclusion is that machine learning offers a promising, alternative technique in property valuation and appraisal research especially in relation to property price prediction.
{"title":"Predicting property prices with machine learning algorithms","authors":"Winky K.O. Ho, B. Tang, S. Wong","doi":"10.1080/09599916.2020.1832558","DOIUrl":"https://doi.org/10.1080/09599916.2020.1832558","url":null,"abstract":"ABSTRACT This study uses three machine learning algorithms including, support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM) in the appraisal of property prices. It applies these methods to examine a data sample of about 40,000 housing transactions in a period of over 18 years in Hong Kong, and then compares the results of these algorithms. In terms of predictive power, RF and GBM have achieved better performance when compared to SVM. The three performance metrics including mean squared error (MSE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) associated with these two algorithms also unambiguously outperform those of SVM. However, our study has found that SVM is still a useful algorithm in data fitting because it can produce reasonably accurate predictions within a tight time constraint. Our conclusion is that machine learning offers a promising, alternative technique in property valuation and appraisal research especially in relation to property price prediction.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2020.1832558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47711319","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 : 2020-12-01DOI: 10.1080/09599916.2020.1838600
Carsten Fritz, Cay Oertel
ABSTRACT This study presents a quantitative analysis of the so-called AR-GARCH-EVT-Copula model aimed at forecasting risk metrics for multi-asset portfolios, including securitised real estate positions. The model incorporates a non-linear dependence structure and time-varying volatility in asset returns. Accordingly, an empirical study using data from six major global markets is carried out. The approach is applied to forecast risk metrics, in comparison to classical methods like historical simulation and variance-covariance models. Forecasts are then compared with realised returns, to calculate hit sequences and conduct statistical interference on the respective models. It is empirically shown that, the AR-GARCH-EVT-Copula model provides a superior forecast concerning risk metrics. This is mainly due to the usage of copulas, allowing us to individually model the dependence structure of random variables. Back testing and test results confirm the superiority of our model in comparison with classic methods such as historical simulation and Variance-Covariance approach. The decomposition of the univariate and multivariate models of the target model reveal the necessity to allow for high order and thus long-lasting autoregressive modelling as well as asymmetric tail dependence and rotated copulae across different portfolios.
{"title":"AR-GARCH-EVT-Copula for securitised real estate: an approach to improving risk forecasts?","authors":"Carsten Fritz, Cay Oertel","doi":"10.1080/09599916.2020.1838600","DOIUrl":"https://doi.org/10.1080/09599916.2020.1838600","url":null,"abstract":"ABSTRACT This study presents a quantitative analysis of the so-called AR-GARCH-EVT-Copula model aimed at forecasting risk metrics for multi-asset portfolios, including securitised real estate positions. The model incorporates a non-linear dependence structure and time-varying volatility in asset returns. Accordingly, an empirical study using data from six major global markets is carried out. The approach is applied to forecast risk metrics, in comparison to classical methods like historical simulation and variance-covariance models. Forecasts are then compared with realised returns, to calculate hit sequences and conduct statistical interference on the respective models. It is empirically shown that, the AR-GARCH-EVT-Copula model provides a superior forecast concerning risk metrics. This is mainly due to the usage of copulas, allowing us to individually model the dependence structure of random variables. Back testing and test results confirm the superiority of our model in comparison with classic methods such as historical simulation and Variance-Covariance approach. The decomposition of the univariate and multivariate models of the target model reveal the necessity to allow for high order and thus long-lasting autoregressive modelling as well as asymmetric tail dependence and rotated copulae across different portfolios.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2020.1838600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45913825","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 : 2020-11-23DOI: 10.1080/09599916.2020.1844784
Garrison Hongyu Song, A. Mussa
ABSTRACT In this paper, we set up a two-sided random search model to study the trading behaviour between buyers and sellers in the commercial real estate market. Our model shows that the interaction between search and bargaining has a profound influence on the role played by the market search friction regarding the equilibrium transaction price of commercial property and the well-being of both buyers and sellers. Against the traditional view that the existence of search friction always plays a negative role in the commercial real estate market, we find that under some conditions the market search friction can increase the equilibrium transaction price of commercial property and thus benefit sellers, which is considered the most valuable especially during the downturn of a business cycle.
{"title":"Commercial Real Estate Market with Two-sided Random Search: Theory and Implications","authors":"Garrison Hongyu Song, A. Mussa","doi":"10.1080/09599916.2020.1844784","DOIUrl":"https://doi.org/10.1080/09599916.2020.1844784","url":null,"abstract":"ABSTRACT In this paper, we set up a two-sided random search model to study the trading behaviour between buyers and sellers in the commercial real estate market. Our model shows that the interaction between search and bargaining has a profound influence on the role played by the market search friction regarding the equilibrium transaction price of commercial property and the well-being of both buyers and sellers. Against the traditional view that the existence of search friction always plays a negative role in the commercial real estate market, we find that under some conditions the market search friction can increase the equilibrium transaction price of commercial property and thus benefit sellers, which is considered the most valuable especially during the downturn of a business cycle.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2020.1844784","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44052351","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 : 2020-10-26DOI: 10.1080/09599916.2020.1837209
Jan Reinert
ABSTRACT The aim of this paper was to compare valuation accuracy of eight European markets, using the same time period, data source and methodology. The emphasis was placed on the accuracy of held properties because previous studies showed that sold properties tend to be valued closer to the market. Real sales data was used to derive hedonic sale prices. The Heckman correction was employed to correct for sample selection bias. A comparison of simple differences between actual valuations and fitted prices showed that valuations were on average below fitted prices in all countries except the Netherlands, indicating a possible overvaluation problem of held properties in Europe. A comparison of the absolute difference showed that the Netherlands and Switzerland displayed the highest valuation accuracy. Italy and Sweden on the other hand were the markets with the lowest median valuation accuracy and largest spreads of observations. All countries, except Sweden, had a majority of observations within an absolute difference of 20%. The two most interesting conclusions from the analysis were that Germany and Switzerland did not differ significantly from other markets in terms of valuation accuracy and that Sweden was consistently the market with the lowest valuation accuracy.
{"title":"Valuation accuracy across Europe: a mass appraisal approach","authors":"Jan Reinert","doi":"10.1080/09599916.2020.1837209","DOIUrl":"https://doi.org/10.1080/09599916.2020.1837209","url":null,"abstract":"ABSTRACT The aim of this paper was to compare valuation accuracy of eight European markets, using the same time period, data source and methodology. The emphasis was placed on the accuracy of held properties because previous studies showed that sold properties tend to be valued closer to the market. Real sales data was used to derive hedonic sale prices. The Heckman correction was employed to correct for sample selection bias. A comparison of simple differences between actual valuations and fitted prices showed that valuations were on average below fitted prices in all countries except the Netherlands, indicating a possible overvaluation problem of held properties in Europe. A comparison of the absolute difference showed that the Netherlands and Switzerland displayed the highest valuation accuracy. Italy and Sweden on the other hand were the markets with the lowest median valuation accuracy and largest spreads of observations. All countries, except Sweden, had a majority of observations within an absolute difference of 20%. The two most interesting conclusions from the analysis were that Germany and Switzerland did not differ significantly from other markets in terms of valuation accuracy and that Sweden was consistently the market with the lowest valuation accuracy.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2020.1837209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46605844","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 : 2020-10-01DOI: 10.1080/09599916.2020.1826561
A. Goodman, Brent C. Smith
ABSTRACT This article examines the U.S. market for Medical Office Buildings (MOB), a segment of the office market that has received little attention in the academic literature. Our attention is directed towards the impact of Certificate-of-Need (CON), a set of state level distortionary public laws that regulate health services planning. With respect to real estate, we find CON regulations increase rents and sales prices medical office building (MOB) rental rates. What makes these findings particularly interesting is that none of the states that currently have CON legislation in place have any language restricting MOB development. The empirical findings suggest that there is a supply constraint due to CON that has a distortionary effect on the MOB market.
{"title":"Distortions in a segment of the commercial office market: the case of medical office buildings","authors":"A. Goodman, Brent C. Smith","doi":"10.1080/09599916.2020.1826561","DOIUrl":"https://doi.org/10.1080/09599916.2020.1826561","url":null,"abstract":"ABSTRACT This article examines the U.S. market for Medical Office Buildings (MOB), a segment of the office market that has received little attention in the academic literature. Our attention is directed towards the impact of Certificate-of-Need (CON), a set of state level distortionary public laws that regulate health services planning. With respect to real estate, we find CON regulations increase rents and sales prices medical office building (MOB) rental rates. What makes these findings particularly interesting is that none of the states that currently have CON legislation in place have any language restricting MOB development. The empirical findings suggest that there is a supply constraint due to CON that has a distortionary effect on the MOB market.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2020.1826561","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46151064","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 : 2020-08-26DOI: 10.1080/09599916.2020.1807587
A. Krause, A. Martín, M. Fix
ABSTRACT Point estimates from Automated Valuation Models (AVMs) represent the most likely value from a distribution of possible values. The uncertainty in the point estimate – the width of the range of possible values at a given level of confidence – is a critical piece of the AVM output, especially in collateral and transactional situations. Estimating AVM uncertainty, however, remains highly unstandardised in both terminology and methods. In this paper, we present and compare two of the most common approaches to estimating AVM uncertainty – model-based and error-based prediction intervals. We also present a uniform language and framework for evaluating the calibration and efficiency of uncertainty estimates. Based on empirical tests on a large, longitudinal dataset of home sales, we show that model-based approaches outperform error-based ones in all but cases with very highest confidence level requirements. The differences between the two methods are conditioned on model class, geographic data partitions and data filtering conditions.
{"title":"Uncertainty in automated valuation models: Error-based versus model-based approaches","authors":"A. Krause, A. Martín, M. Fix","doi":"10.1080/09599916.2020.1807587","DOIUrl":"https://doi.org/10.1080/09599916.2020.1807587","url":null,"abstract":"ABSTRACT Point estimates from Automated Valuation Models (AVMs) represent the most likely value from a distribution of possible values. The uncertainty in the point estimate – the width of the range of possible values at a given level of confidence – is a critical piece of the AVM output, especially in collateral and transactional situations. Estimating AVM uncertainty, however, remains highly unstandardised in both terminology and methods. In this paper, we present and compare two of the most common approaches to estimating AVM uncertainty – model-based and error-based prediction intervals. We also present a uniform language and framework for evaluating the calibration and efficiency of uncertainty estimates. Based on empirical tests on a large, longitudinal dataset of home sales, we show that model-based approaches outperform error-based ones in all but cases with very highest confidence level requirements. The differences between the two methods are conditioned on model class, geographic data partitions and data filtering conditions.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2020.1807587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48440921","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 : 2020-08-02DOI: 10.1080/09599916.2020.1794936
N. Kishor
ABSTRACT This paper provides a modelling framework to examine the very low correlation at short horizons and high correlation at long horizons between private and public commercial real estate returns. For this purpose, we use a correlated, unobserved component model with a common trend and Markov-switching heteroskedasticity. This model decomposes the public and private commercial real estate prices into a common trend and interdependent cycles. The proposed model is able to endogenously capture low and high volatility regimes in real estate markets. More importantly, our model shows that the low correlation observed at short horizons between the public and private real estate markets is mainly due to the absence of any correlation in low-volatility regimes. On the other hand, the cycles, or short-run movements, in these two markets are highly correlated in high-volatility regimes.
{"title":"Understanding the relationship between public and private commercial real estate markets","authors":"N. Kishor","doi":"10.1080/09599916.2020.1794936","DOIUrl":"https://doi.org/10.1080/09599916.2020.1794936","url":null,"abstract":"ABSTRACT This paper provides a modelling framework to examine the very low correlation at short horizons and high correlation at long horizons between private and public commercial real estate returns. For this purpose, we use a correlated, unobserved component model with a common trend and Markov-switching heteroskedasticity. This model decomposes the public and private commercial real estate prices into a common trend and interdependent cycles. The proposed model is able to endogenously capture low and high volatility regimes in real estate markets. More importantly, our model shows that the low correlation observed at short horizons between the public and private real estate markets is mainly due to the absence of any correlation in low-volatility regimes. On the other hand, the cycles, or short-run movements, in these two markets are highly correlated in high-volatility regimes.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2020.1794936","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46197208","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 : 2020-07-28DOI: 10.1080/09599916.2020.1790631
Soosung Hwang, Youngha Cho, Jinho Shin
ABSTRACT
We investigate if house prices are affected by the overconfidence of households who predict house prices using imperfect public information about economic outlook. For this purpose, we develop a new measure of household overconfidence in the Bayesian framework. For the three variables we test – changes in consumption, stock returns, and changes in human capital, we find that UK households were overconfident about the signals of consumption regardless of regions. However, households in London were overconfident about the signals of stock markets whereas those remote from London were overconfident about the signals of human capital. The results of household overconfidence appear positive in the UK housing market for our sample period from 1980 to 2018, in particular, 0.5% per quarter in London.
{"title":"The impact of UK household overconfidence in public information on house prices","authors":"Soosung Hwang, Youngha Cho, Jinho Shin","doi":"10.1080/09599916.2020.1790631","DOIUrl":"https://doi.org/10.1080/09599916.2020.1790631","url":null,"abstract":"<p><b>ABSTRACT</b></p> <p>We investigate if house prices are affected by the overconfidence of households who predict house prices using imperfect public information about economic outlook. For this purpose, we develop a new measure of household overconfidence in the Bayesian framework. For the three variables we test – changes in consumption, stock returns, and changes in human capital, we find that UK households were overconfident about the signals of consumption regardless of regions. However, households in London were overconfident about the signals of stock markets whereas those remote from London were overconfident about the signals of human capital. The results of household overconfidence appear positive in the UK housing market for our sample period from 1980 to 2018, in particular, 0.5% per quarter in London.</p>","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138529691","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 : 2020-07-02DOI: 10.1080/09599916.2020.1794935
P. Mcallister
ABSTRACT This paper focuses on how brokerage practices in the markets for commercial real estate investment assets and residential development land in England generate different possibilities for and patterns of opportunistic behaviours. Drawing upon previous research and analysis, the paper examines the nature of the brokerage market in both sub-markets. An interactionist model of the determinants of ethical judgement in the context of the brokerage sector is created. Based upon an interview survey of brokers and principals, the findings of an exploratory empirical study are discussed. Whilst there are challenges in defining, observing and measuring opportunistic behaviours, it is concluded that a number of different unethical practices have become routine rather than exceptional in the markets for institutional-grade real estate assets and residential development land.
{"title":"Can brokers rig the real estate market? An exploratory study of the commercial real estate sector","authors":"P. Mcallister","doi":"10.1080/09599916.2020.1794935","DOIUrl":"https://doi.org/10.1080/09599916.2020.1794935","url":null,"abstract":"ABSTRACT This paper focuses on how brokerage practices in the markets for commercial real estate investment assets and residential development land in England generate different possibilities for and patterns of opportunistic behaviours. Drawing upon previous research and analysis, the paper examines the nature of the brokerage market in both sub-markets. An interactionist model of the determinants of ethical judgement in the context of the brokerage sector is created. Based upon an interview survey of brokers and principals, the findings of an exploratory empirical study are discussed. Whilst there are challenges in defining, observing and measuring opportunistic behaviours, it is concluded that a number of different unethical practices have become routine rather than exceptional in the markets for institutional-grade real estate assets and residential development land.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2020.1794935","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49176046","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 : 2020-05-25DOI: 10.1080/09599916.2020.1767681
A. Khazal, Ole Jakob Sønstebø, J. Olaussen, A. Oust
ABSTRACT In the Norwegian real estate market, used dwellings are normally sold through an auction process similar to the standard English (open ascending-bid) auction. Using survey results (N = 1,803), we define jump bids and investigate the motivations behind the use of such strategies. We find that most bidders tend to consider intimidation and signalling as the main motivations for applying a jump-bidding strategy, and intimidation strategies applied by competing bidders appear to be an important reason for bidders withdrawing early from an auction. We also use a sample of 1,142 auction journals and find that, on average, auctions containing jump bids achieve 2.8–9.3 percent higher price premiums compared to strictly straightforward-bidding auctions. The premium is higher when the intimidation strategy fails and competing bidders counter with jump bids. Additionally, this paper provides evidence that jump bids are usually placed at the earliest stage of the auction and have a stronger intimidation effect the earlier they are placed, despite having an overall positive effect on the premium. The results are robust to different valuation approaches and omitted variable bias controls. Our findings have important implications for sellers and buyers in auction settings, and for regulators of auction processes.
{"title":"The impact of strategic jump bidding in residential English auctions","authors":"A. Khazal, Ole Jakob Sønstebø, J. Olaussen, A. Oust","doi":"10.1080/09599916.2020.1767681","DOIUrl":"https://doi.org/10.1080/09599916.2020.1767681","url":null,"abstract":"ABSTRACT In the Norwegian real estate market, used dwellings are normally sold through an auction process similar to the standard English (open ascending-bid) auction. Using survey results (N = 1,803), we define jump bids and investigate the motivations behind the use of such strategies. We find that most bidders tend to consider intimidation and signalling as the main motivations for applying a jump-bidding strategy, and intimidation strategies applied by competing bidders appear to be an important reason for bidders withdrawing early from an auction. We also use a sample of 1,142 auction journals and find that, on average, auctions containing jump bids achieve 2.8–9.3 percent higher price premiums compared to strictly straightforward-bidding auctions. The premium is higher when the intimidation strategy fails and competing bidders counter with jump bids. Additionally, this paper provides evidence that jump bids are usually placed at the earliest stage of the auction and have a stronger intimidation effect the earlier they are placed, despite having an overall positive effect on the premium. The results are robust to different valuation approaches and omitted variable bias controls. Our findings have important implications for sellers and buyers in auction settings, and for regulators of auction processes.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2020.1767681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41601030","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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