Pub Date : 2019-04-03DOI: 10.1080/09599916.2019.1593220
Qiulin Ke, Karen Sieracki
ABSTRACT We investigates the sentiment-driven trading behaviour of the four types of investors in the London office market, i.e. UK institutional investors, UK private investors, UK listed real estate companies/Real Estate Investment Trust (REIT)s and overseas investors. In addition, we examine the relationship between investor sentiment and property performance. Related indices are calculated to examine the existence of herding behaviour of different investors. We find that UK private investors follow a contrarian strategy to UK institutional investors and listed real estate companies/REITs and enter/exit the market at different points of time. UK institutional investors tend to follow the sentiment of UK listed real estate companies/REITs and overseas investors with lags. There is no evidence that overseas investors rely upon the sentiment of UK specialised property investors in their decision-making. We find the sentiment of different investors is influenced differently by market fundamentals. Yield and rental growth rate have significant impact on trading activity of overseas investors, but not on other investors. The stock market return and securitised real estate return have significant impact on the trading activity of UK institutional investor and overseas investor, but have no significant influence on the trading behaviour of UK private investor and listed real estate company/REIT.
{"title":"Exploring sentiment-driven trading behaviour of different types of investors in the London office market","authors":"Qiulin Ke, Karen Sieracki","doi":"10.1080/09599916.2019.1593220","DOIUrl":"https://doi.org/10.1080/09599916.2019.1593220","url":null,"abstract":"ABSTRACT We investigates the sentiment-driven trading behaviour of the four types of investors in the London office market, i.e. UK institutional investors, UK private investors, UK listed real estate companies/Real Estate Investment Trust (REIT)s and overseas investors. In addition, we examine the relationship between investor sentiment and property performance. Related indices are calculated to examine the existence of herding behaviour of different investors. We find that UK private investors follow a contrarian strategy to UK institutional investors and listed real estate companies/REITs and enter/exit the market at different points of time. UK institutional investors tend to follow the sentiment of UK listed real estate companies/REITs and overseas investors with lags. There is no evidence that overseas investors rely upon the sentiment of UK specialised property investors in their decision-making. We find the sentiment of different investors is influenced differently by market fundamentals. Yield and rental growth rate have significant impact on trading activity of overseas investors, but not on other investors. The stock market return and securitised real estate return have significant impact on the trading activity of UK institutional investor and overseas investor, but have no significant influence on the trading behaviour of UK private investor and listed real estate company/REIT.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2019-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2019.1593220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46757868","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 : 2019-03-19DOI: 10.1080/09599916.2019.1590454
Cath Jackson, A. Orr
ABSTRACT It is widely established that economic policy uncertainty (EPU) affects investment decisions and performance, yet research in this area has overlooked the direct property investment market. This article seeks to rectify this and proposes a multistage multilevel analytical framework to offer new insights and a richness of findings. Using a news-based measure of EPU in the United Kingdom, and controlling for economic conditions, a national-level analysis reveals some evidence of Granger-Causality between EPU and total returns, indicating that pricing is responsive to uncertainty. These findings suggest that EPU is an important risk factor for direct property investments, with pricing implications. Differences in data and performance measure are important, however, with income returns unresponsive. A micro-level investigation begins to reveal some of the asset-pricing decisions underpinning the national results, indicating investors’ concerns for income streams are consistently high, regardless of varying EPU. Pricing can also cause changes in EPU, such as in the retail and industrial markets (increasingly linked through logistics) reflecting sector-specific stakeholder groups and newsworthy issues. This evidence highlights how important it is for policy-makers to understand the complex and bi-directional relationship, that indecision can undermine investment confidence and cause investment market volatility, in turn raising EPU.
{"title":"Investment decision-making under economic policy uncertainty","authors":"Cath Jackson, A. Orr","doi":"10.1080/09599916.2019.1590454","DOIUrl":"https://doi.org/10.1080/09599916.2019.1590454","url":null,"abstract":"ABSTRACT It is widely established that economic policy uncertainty (EPU) affects investment decisions and performance, yet research in this area has overlooked the direct property investment market. This article seeks to rectify this and proposes a multistage multilevel analytical framework to offer new insights and a richness of findings. Using a news-based measure of EPU in the United Kingdom, and controlling for economic conditions, a national-level analysis reveals some evidence of Granger-Causality between EPU and total returns, indicating that pricing is responsive to uncertainty. These findings suggest that EPU is an important risk factor for direct property investments, with pricing implications. Differences in data and performance measure are important, however, with income returns unresponsive. A micro-level investigation begins to reveal some of the asset-pricing decisions underpinning the national results, indicating investors’ concerns for income streams are consistently high, regardless of varying EPU. Pricing can also cause changes in EPU, such as in the retail and industrial markets (increasingly linked through logistics) reflecting sector-specific stakeholder groups and newsworthy issues. This evidence highlights how important it is for policy-makers to understand the complex and bi-directional relationship, that indecision can undermine investment confidence and cause investment market volatility, in turn raising EPU.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2019-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2019.1590454","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48269434","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 : 2019-01-02DOI: 10.1080/09599916.2019.1587489
J. Pérez-Rave, J. C. Correa-Morales, Favián González-Echavarría
ABSTRACT The hedonic price regressions have mainly been used for inference. In contrast, machine learning employed on big data has a great potential for prediction. To contribute to the integration of these two strategies, this article proposes a machine learning approach to the regression analysis of big data, viz. real estate prices, for both inferential and predictive purposes. The methodology incorporates a new procedure of selecting variables, called ‘incremental sample with resampling’ (MINREM). The methodology is tested on two cases. The first is data from web advertisements selling used homes in Colombia (61,826 observations). The second considers the data (58,888 observations) from a sample of the Metropolitan American Housing Survey 2011 obtained and prepared by a reference study. The methodology consists of two stages. The first chooses the important variables under MINREM; the second focuses on the traditional training and validation procedure for machine learning, adding three activities. In both test cases, the methodology shows its value for obtaining highly parsimonious and stable models for different sample sizes, as well as taking advantage of the inferential and predictive use of the obtained regression functions. This paper contributes to an original methodology for big data regression analysis.
{"title":"A machine learning approach to big data regression analysis of real estate prices for inferential and predictive purposes","authors":"J. Pérez-Rave, J. C. Correa-Morales, Favián González-Echavarría","doi":"10.1080/09599916.2019.1587489","DOIUrl":"https://doi.org/10.1080/09599916.2019.1587489","url":null,"abstract":"ABSTRACT The hedonic price regressions have mainly been used for inference. In contrast, machine learning employed on big data has a great potential for prediction. To contribute to the integration of these two strategies, this article proposes a machine learning approach to the regression analysis of big data, viz. real estate prices, for both inferential and predictive purposes. The methodology incorporates a new procedure of selecting variables, called ‘incremental sample with resampling’ (MINREM). The methodology is tested on two cases. The first is data from web advertisements selling used homes in Colombia (61,826 observations). The second considers the data (58,888 observations) from a sample of the Metropolitan American Housing Survey 2011 obtained and prepared by a reference study. The methodology consists of two stages. The first chooses the important variables under MINREM; the second focuses on the traditional training and validation procedure for machine learning, adding three activities. In both test cases, the methodology shows its value for obtaining highly parsimonious and stable models for different sample sizes, as well as taking advantage of the inferential and predictive use of the obtained regression functions. This paper contributes to an original methodology for big data regression analysis.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2019.1587489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43232346","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 : 2019-01-02DOI: 10.1080/09599916.2019.1568283
K. Liow, Xiaoxiao Zhou, Qiang Li, Yuting Huang
ABSTRACT The novelty of this study is the use of continuous wavelet transform analysis of wavelet coherence, as well as its partial and multiple forms, to revisit the co-movements of Asian-Pacific public real estate markets among themselves and with the US, for a time span which covers the 12 January 1995–23 June 2016 period. Earlier research does not have satisfactory results because traditional methods average different relationships in time domain only. From the wavelet analysis, investors can extract the time-scale that most interests them. We find that the co-movement relationship across the real estate markets increases during the two major crisis period, as well as becomes stronger as the scale increases. Hong Kong and Singapore have the strongest time-scale co-movement relationship. Finally, the influence of domestic macroeconomic factors on real estate return co-movement appears to be greater at the long-term horizons than at the short-term horizons.
{"title":"Co-movement between the US and the securitised real estate markets of the Asian-Pacific economies","authors":"K. Liow, Xiaoxiao Zhou, Qiang Li, Yuting Huang","doi":"10.1080/09599916.2019.1568283","DOIUrl":"https://doi.org/10.1080/09599916.2019.1568283","url":null,"abstract":"ABSTRACT The novelty of this study is the use of continuous wavelet transform analysis of wavelet coherence, as well as its partial and multiple forms, to revisit the co-movements of Asian-Pacific public real estate markets among themselves and with the US, for a time span which covers the 12 January 1995–23 June 2016 period. Earlier research does not have satisfactory results because traditional methods average different relationships in time domain only. From the wavelet analysis, investors can extract the time-scale that most interests them. We find that the co-movement relationship across the real estate markets increases during the two major crisis period, as well as becomes stronger as the scale increases. Hong Kong and Singapore have the strongest time-scale co-movement relationship. Finally, the influence of domestic macroeconomic factors on real estate return co-movement appears to be greater at the long-term horizons than at the short-term horizons.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2019.1568283","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43684891","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 : 2019-01-02DOI: 10.1080/09599916.2019.1590453
L. McCann, N. Hutchison, A. Adair
ABSTRACT The aim of this paper is to consider the sources of finance used to support major capital expenditure in the UK Higher Education sector and to reflect on any differences between traditional corporate finance theory and practice in the UK university sector. Utilising both HESA data returns and published annual accounts, an in-depth analysis using a logit structure is carried out on data from the top 63 UK universities over the period 2014–2017, to establish the range of funding sources adopted for major capital projects, all set within the context of the UK macro environment and a period of low interest rates. The research also carries out a survey of funders to understand the decision criteria used by lenders active in the Higher Education sector and a survey of university finance directors to determine the use of the funds, the reasons behind past lending decisions and to ascertain likely future demand for finance to fund major capital projects.
{"title":"External funding of major capital projects in the UK Higher Education sector: issues of demand, supply and market timing?","authors":"L. McCann, N. Hutchison, A. Adair","doi":"10.1080/09599916.2019.1590453","DOIUrl":"https://doi.org/10.1080/09599916.2019.1590453","url":null,"abstract":"ABSTRACT The aim of this paper is to consider the sources of finance used to support major capital expenditure in the UK Higher Education sector and to reflect on any differences between traditional corporate finance theory and practice in the UK university sector. Utilising both HESA data returns and published annual accounts, an in-depth analysis using a logit structure is carried out on data from the top 63 UK universities over the period 2014–2017, to establish the range of funding sources adopted for major capital projects, all set within the context of the UK macro environment and a period of low interest rates. The research also carries out a survey of funders to understand the decision criteria used by lenders active in the Higher Education sector and a survey of university finance directors to determine the use of the funds, the reasons behind past lending decisions and to ascertain likely future demand for finance to fund major capital projects.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2019.1590453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47090371","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 : 2019-01-02DOI: 10.1080/09599916.2018.1562490
Sören Gröbel
ABSTRACT This paper examines the spatial dependency exhibited by the error term variance of hedonic modeling based on German housing price data. To this end, it applies the spatial autoregressive conditional heteroscedasticity (SARCH) model previously discussed in housing literature, which allows for the consideration of spatial dependency when modeling the error variance of hedonic pricing. This model represents a spatialized version of the well-known ARCH-model used in time series analysis. Consistent with previous findings, this paper confirms the existence of spatial conditional heteroscedasticity, i.e. dependency in the error variance. However, this spatial dependency is not a global phenomenon, but can be ascribed to spatial concentrations of apartments with a relatively high variance in a small number of the same neighborhoods. The analysis of spatial heteroscedasticity helps to improve the estimation efficiency and prediction accuracy. In addition, spatial differences can be used to account for idiosyncratic risk when conducting mass appraisal.
{"title":"Analysis of spatial variance clustering in the hedonic modeling of housing prices","authors":"Sören Gröbel","doi":"10.1080/09599916.2018.1562490","DOIUrl":"https://doi.org/10.1080/09599916.2018.1562490","url":null,"abstract":"ABSTRACT This paper examines the spatial dependency exhibited by the error term variance of hedonic modeling based on German housing price data. To this end, it applies the spatial autoregressive conditional heteroscedasticity (SARCH) model previously discussed in housing literature, which allows for the consideration of spatial dependency when modeling the error variance of hedonic pricing. This model represents a spatialized version of the well-known ARCH-model used in time series analysis. Consistent with previous findings, this paper confirms the existence of spatial conditional heteroscedasticity, i.e. dependency in the error variance. However, this spatial dependency is not a global phenomenon, but can be ascribed to spatial concentrations of apartments with a relatively high variance in a small number of the same neighborhoods. The analysis of spatial heteroscedasticity helps to improve the estimation efficiency and prediction accuracy. In addition, spatial differences can be used to account for idiosyncratic risk when conducting mass appraisal.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2018.1562490","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45985104","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 : 2018-10-02DOI: 10.1080/09599916.2018.1551923
Jochen Hausler, Jessica Ruscheinsky, M. Lang
ABSTRACT This paper examines the relationship between news-based sentiment, captured through a machine learning approach, and the US securitised and direct commercial real estate markets. Thus, we contribute to the literature on text-based sentiment analysis in real estate by creating and testing various sentiment measures by utilising trained support vector networks. Using a vector autoregressive framework, we find the constructed sentiment indicators to predict the total returns of both markets. The results show a leading relationship of our sentiment, even after controlling for macroeconomic factors and other established sentiment proxies. Furthermore, empirical evidence suggests a shorter response time of the indirect market in relation to the direct one. The findings make a valuable contribution to real estate research and industry participants, as we demonstrate the successful application of a sentiment-creation procedure that enables short and flexible aggregation periods. To the best of our knowledge, this is the first study to apply a machine learning approach to capture textual sentiment relevant to US real estate markets.
{"title":"News-based sentiment analysis in real estate: a machine learning approach","authors":"Jochen Hausler, Jessica Ruscheinsky, M. Lang","doi":"10.1080/09599916.2018.1551923","DOIUrl":"https://doi.org/10.1080/09599916.2018.1551923","url":null,"abstract":"ABSTRACT This paper examines the relationship between news-based sentiment, captured through a machine learning approach, and the US securitised and direct commercial real estate markets. Thus, we contribute to the literature on text-based sentiment analysis in real estate by creating and testing various sentiment measures by utilising trained support vector networks. Using a vector autoregressive framework, we find the constructed sentiment indicators to predict the total returns of both markets. The results show a leading relationship of our sentiment, even after controlling for macroeconomic factors and other established sentiment proxies. Furthermore, empirical evidence suggests a shorter response time of the indirect market in relation to the direct one. The findings make a valuable contribution to real estate research and industry participants, as we demonstrate the successful application of a sentiment-creation procedure that enables short and flexible aggregation periods. To the best of our knowledge, this is the first study to apply a machine learning approach to capture textual sentiment relevant to US real estate markets.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2018.1551923","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47094382","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 : 2018-10-02DOI: 10.1080/09599916.2018.1549587
Liesa Schrand, Claudia Ascherl, Wolfgang Schaefers
ABSTRACT Our paper is the first to identify the determinants which explain the presence of women on the board of directors and to study the relationship between gender diversity and financial performance in a US REIT context. We apply a two-stage Heckman approach to a unique panel dataset of 112 US Equity REITs over the period 2005–2015. Our results show that a REIT’s likelihood of having a woman on the board of directors depends strongly on board attributes. Especially institutional investors support gender-diverse leadership teams, which might be driven by the perception that women contribute to an enhanced internal monitoring in the REIT context, in which external monitoring is weakened through ownership restrictions. We find evidence of a U-shaped relationship between gender diversity in executive positions and price per net asset value (PRICE/NAV). In the case of REITs, a critical mass of female executives is reached at approximately 30% representation. This finding holds especially for real estate sectors with a strong consumer orientation and a high proportion of women in the workforce, such as retail and healthcare. Our performance analysis demonstrates that gender diversity has a positive effect on market performance (PRICE/NAV), but not on operating performance (FFO/SHARE).
{"title":"Gender diversity and financial performance: evidence from US REITs","authors":"Liesa Schrand, Claudia Ascherl, Wolfgang Schaefers","doi":"10.1080/09599916.2018.1549587","DOIUrl":"https://doi.org/10.1080/09599916.2018.1549587","url":null,"abstract":"ABSTRACT Our paper is the first to identify the determinants which explain the presence of women on the board of directors and to study the relationship between gender diversity and financial performance in a US REIT context. We apply a two-stage Heckman approach to a unique panel dataset of 112 US Equity REITs over the period 2005–2015. Our results show that a REIT’s likelihood of having a woman on the board of directors depends strongly on board attributes. Especially institutional investors support gender-diverse leadership teams, which might be driven by the perception that women contribute to an enhanced internal monitoring in the REIT context, in which external monitoring is weakened through ownership restrictions. We find evidence of a U-shaped relationship between gender diversity in executive positions and price per net asset value (PRICE/NAV). In the case of REITs, a critical mass of female executives is reached at approximately 30% representation. This finding holds especially for real estate sectors with a strong consumer orientation and a high proportion of women in the workforce, such as retail and healthcare. Our performance analysis demonstrates that gender diversity has a positive effect on market performance (PRICE/NAV), but not on operating performance (FFO/SHARE).","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2018.1549587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43739938","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 : 2018-10-02DOI: 10.1080/09599916.2018.1513057
Theis Theisen, A. Emblem
ABSTRACT Parents accompany children to day-care, implying costs of time and money. Distance to kindergarten may therefore be an important locational attribute, which is likely to be discounted into house prices. We account for this through a theoretical model of house price formation, incorporating not only monetary and time costs associated with accompanying children to a kindergarten, but also possibly negative external effects of kindergartens on their immediate vicinity. Our theoretical model predicts that house prices increase as distance to kindergarten decreases, reach a peak, and then decline as one come very close to a kindergarten. We use a large sample of house transactions from a Norwegian town to explore the relationship between house prices and the distance to kindergarten. The empirical results support the prediction that house prices decline as distance to kindergarten increases, but we find no significant drop in house prices in the immediate vicinity of kindergartens. The results may be of interest to several actors in real-estate markets, perhaps particularly to urban planners and real-estate developers when considering the location of kindergartens.
{"title":"House prices and proximity to kindergarten – costs of distance and external effects?","authors":"Theis Theisen, A. Emblem","doi":"10.1080/09599916.2018.1513057","DOIUrl":"https://doi.org/10.1080/09599916.2018.1513057","url":null,"abstract":"ABSTRACT Parents accompany children to day-care, implying costs of time and money. Distance to kindergarten may therefore be an important locational attribute, which is likely to be discounted into house prices. We account for this through a theoretical model of house price formation, incorporating not only monetary and time costs associated with accompanying children to a kindergarten, but also possibly negative external effects of kindergartens on their immediate vicinity. Our theoretical model predicts that house prices increase as distance to kindergarten decreases, reach a peak, and then decline as one come very close to a kindergarten. We use a large sample of house transactions from a Norwegian town to explore the relationship between house prices and the distance to kindergarten. The empirical results support the prediction that house prices decline as distance to kindergarten increases, but we find no significant drop in house prices in the immediate vicinity of kindergartens. The results may be of interest to several actors in real-estate markets, perhaps particularly to urban planners and real-estate developers when considering the location of kindergartens.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2018.1513057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59502960","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 : 2018-07-03DOI: 10.1080/09599916.2018.1511628
Ling-Hin Li, K. Cheung, Sue Yurim Han
ABSTRACT Hong Kong as a small city has witnessed a drastic change in the number of short-stay and same-day tourists from Mainland China ever since the relaxation of the tourism policy began in the early 2000’s. This study examines the impacts on the prices of retail space attributed to the substantial increase of cross-border shoppers. Based on a comprehensive retail property transaction records in Hong Kong and a semi-log regression model, our study confirms a positive impact of the number of cross-border shoppers on retail property prices, especially on the value of newer and larger-sized street-level retail shops. Moreover, we find that the effects brought on the retail property market are city-wide and not limited to specific districts which are relatively closer to the border with Shenzhen and with a higher degree of accessibility by these cross-border shoppers. This paper is limited by a number of assumptions including travel distance of the shoppers from Shenzhen. Nevertheless, with an increase in personal travels by the affluent Mainland Chinese citizens who usually spend a lot on shopping outside China, future studies can be made in other North American or European cities for comparison.
{"title":"The impacts of cross-border tourists on local retail property market: an empirical analysis of Hong Kong","authors":"Ling-Hin Li, K. Cheung, Sue Yurim Han","doi":"10.1080/09599916.2018.1511628","DOIUrl":"https://doi.org/10.1080/09599916.2018.1511628","url":null,"abstract":"ABSTRACT Hong Kong as a small city has witnessed a drastic change in the number of short-stay and same-day tourists from Mainland China ever since the relaxation of the tourism policy began in the early 2000’s. This study examines the impacts on the prices of retail space attributed to the substantial increase of cross-border shoppers. Based on a comprehensive retail property transaction records in Hong Kong and a semi-log regression model, our study confirms a positive impact of the number of cross-border shoppers on retail property prices, especially on the value of newer and larger-sized street-level retail shops. Moreover, we find that the effects brought on the retail property market are city-wide and not limited to specific districts which are relatively closer to the border with Shenzhen and with a higher degree of accessibility by these cross-border shoppers. This paper is limited by a number of assumptions including travel distance of the shoppers from Shenzhen. Nevertheless, with an increase in personal travels by the affluent Mainland Chinese citizens who usually spend a lot on shopping outside China, future studies can be made in other North American or European cities for comparison.","PeriodicalId":45726,"journal":{"name":"Journal of Property Research","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09599916.2018.1511628","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43144189","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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