Spatial Variability of the ‘Airbnb Effect’: A Spatially Explicit Analysis of Airbnb’s Impact on Housing Prices in Sydney

Over the last decade, the emergence and significant growth of home-sharing platforms, such as Airbnb, has coincided with rising housing unaffordability in many global cities. It is in this context that we look to empirically assess the impact of Airbnb on housing prices in Sydney—one of the least affordable cities in the world. Employing a hedonic property valuation model, our results indicate that Airbnb’s overall effect is positive. A 1% increase in Airbnb density is associated with approximately a 2% increase in property sales price. However, recognizing that Airbnb’s effect is geographically uneven and given the fragmented nature of Sydney’s housing market, we also employ a GWR to account for the spatial variation in Airbnb activity. The findings confirm that Airbnb’s influence on housing prices is varied across the city. Sydney’s northern beaches and parts of western Sydney experience a statistically significant value uplift attributable to Airbnb activity. However, traditional tourist locations focused around Sydney’s CBD and the eastern suburbs experience insignificant or negative property price impacts. The results highlight the need for policymakers to consider local Airbnb and housing market contexts when deciding the appropriate level and design of Airbnb regulation.

Information Opportunities of Administrative Data on Residential Real Estate Transactions for Calculating the House Prices in Russia’s Housing Market

Purpose of the study. Development, justification and testing of a methodology for improving statistical monitoring of average prices in the Russian housing market, based on the use of registration information of the Unified State Register of Real Estate (USRN) on transactions for the purchase of residential real estate, in accordance with international statistical standards for Residential Property Price statistics.Materials and methods. The theoretical basis of the study was the United Nations system of national accounts (version of 2008), including the European system of accounts as amended in 2010. The research methodological base was made up of official statistical sources: metadata and international statistics guidelines in the field of national accounting, Handbook on Residential Property Price Indices and related housing indicators, as well as methodological provisions and an album of Rosstat forms, and methodological materials of the administrative statistics of the Federal Service for State Registration, Cadastre and Cartography of the Russian Federation (Rosreestr). The depersonalized registration data on households’ market transactions of the Unified State Register of Property Rights and Transactions maintaining by Rosreestr were used as an information database of the research.Results. The main result of the study is the design and substantiation of a system of indicators for the construction of an integrated information source for Residential Property Price statistics, on the base on interdepartmental information interaction.Conclusion. The proposed system of indicators will provide a highquality database that could be used in order to construct constant quality House Prices for various types of homogeneous residential property in the housing market, complying with the concepts of international statistical standards.

Spatial Variability of the ‘Airbnb Effect’: A Spatially Explicit Analysis of Airbnb's impact on Housing Prices in Sydney

Over the last decade, the emergence and significant growth of home sharing platforms such as Airbnb has coincided with rising housing unaffordability in many global cities. It is in this context that we look to empirically assess the impact of Airbnb on housing prices in Sydney - one of the least affordable cities in the world. Employing a hedonic property valuation model, our results indicate that Airbnb’s overall effect is positive. A 1% increase in Airbnb density is associated with approximately a 2% increase in property sales price. However, recognising that Airbnb’s effect is geographically uneven and given the fragmented nature of Sydney’s housing market, we also employ a GWR to account for the spatial variation in Airbnb activity. The findings confirm that Airbnb’s influence on housing prices is varied across the city. Sydney’s northern beaches and parts of western Sydney experience a statistically significant value uplift attributable to Airbnb activity. However, traditional tourist locations focused around Sydney’s CBD and the eastern suburbs experience insignificant or negative property price impacts. The results highlight the need for policymakers to consider local Airbnb and housing market contexts when deciding the appropriate level and design of Airbnb regulation.

REVEALING THE INVESTMENT VALUE OF PENANG HERITAGE PROPERTIES

Heritage buildings are a representation of historic features and the Malaysian culture. The intangible value of a heritage property comprises aesthetic quality, spiritual aspects, social functions, and its own uniqueness. Therefore, heritage properties have been seen to be moving away from traditional alternative investments, which are not covered by conventional real estate schemes. Additionally, the characteristics of heritage properties are expected to be seen as ‘art’, and they offer a highly beneficial diversification strategy with a relatively low correlation towards traditional assets classes. The Penang (Island) Heritage Property Price Index (PPHPPI) is estimated to be using a hedonic regression method. Based on the index, the heritage property records the highest quarterly returns and risk among the conventional assets considered in this study.

THE DEVELOPMENT OF PENANG SHOP PRICE INDEX (PSPI) USING LASPEYRES HEDONIC PRICE MODEL

The index is considered an important benchmark and is a decision-making tool in the financial and capital markets, as well as in the property market. In Malaysia, continuous monitoring of property price movements is important as almost half of banking exposure is on property. Further, NAPIC has published indicators displaying the performance of property such as MHPI and PBO-RI. However, indicators regarding the price of commercial property are still less widely published in Malaysia. This study was conducted to develop indicators related to the price of commercial property, especially to shop property. This study has focused on the state of Penang as a study area. The literature review methodology is used to identify existing methods and practices used in developing the index of commercial property both in Malaysia and internationally. In determining the appropriate form of hedonic functions for the development of PSPI, analysis of dependent and independent variables was performed. Meanwhile, the development of the index is based on the Laspeyres hedonic model which is the same as the development of MHPI and PBO-RI. The development of PSPI will be able to help the industry and investors to make decisions and benchmark the performance of shop. This is also one of the pilot studies in Malaysia to form an indicator of commercial property.

MACHINE LEARNING FOR PROPERTY PRICE PREDICTION AND PRICE VALUATION: A SYSTEMATIC LITERATURE REVIEW

Machine learning is a branch of artificial intelligence that allows software applications to be more accurate in its data predicting, as well as to predict current performance and improve for future data. This study reviews published articles with the application of machine learning techniques for price prediction and valuation. Authors seek to explore optimal solutions in predicting the property price indices, that will be beneficial to the policymakers in assessing the overall economic situation. This study also looks into the use of machine learning in property valuation towards identifying the best model in predicting property values based on its characteristics such as location, land size, number of rooms and others. A systematic review was conducted to review previous studies that imposed machine learning as statistical tool in predicting and valuing property prices. Articles on real estate price prediction and price valuation using machine learning techniques were observed using electronics database. The finding shows the increasing use of this method in the real estate field. The most successful machine learning algorithms used is the Random Forest that has better compatibility to the data situation.

Housing prices in emerging countries during COVID-19: evidence from Turkey

Purpose By considering the rapid and continuous increase of housing prices in Turkey recently, this study aims to examine the determinants of the residential property price index (RPPI). In this context, a total of 12 explanatory (3 macroeconomic, 8 markets and 1 pandemic) variables are included in the analysis. Moreover, the residential property price index for new dwellings (NRPPI) and the residential property price index for old dwellings (ORPPI) are considered for robustness checks. Design/methodology/approach A quantile regression (QR) model is used to examine the main determinants of RPPI in Turkey. A monthly time series data set for the period between January 2010 and October 2020 is included. Moreover, NRPPI and ORPPI are examined for robustness. Findings Predictions for RPPI, NRPPI and ORPPI are carried out separately at the country (Turkey) level. The results show that market variables are more important than macroeconomic variables; the pandemic and rent have the highest effect on the indices; The effects of the explanatory variables on housing prices do not change much from low to high levels, the COVID-19 pandemic and weighted average cost of funding have a decreasing effect on indices while other variables have an increasing effect in low quantiles; the pandemic and monetary policy indicators have a negative and significant effect in low quantiles whereas they are not effective in high quantiles; the results for RPPI, NRPPI and ORPPI are consistent and robust. Research limitations/implications The results of the study emphasize the importance of the pandemic, rent, monetary policy indicators and interest rates on the indices, respectively. On the other hand, focusing solely on Turkey and excluding global variables is the main limitation of this study. Therefore, the authors encourage researchers to work on other emerging countries by considering global variables. Hence, future studies may extend this study. Practical implications The COVID-19 pandemic and market variables are determined as influential variables on housing prices in Turkey whereas macroeconomic variables are not effective, which does not mean that macroeconomic variables can be fully ignored. Hence, the main priority should be on focusing on market variables by also considering the development in macroeconomic variables. Social implications Emerging countries can make housing prices stable and affordable, which will increase homeownership. Hence, they can benefit from stability in housing markets. Originality/value The QR method is performed for the first time to examine housing prices in Turkey at the country level according to the existing literature. The results obtained from the QR analysis and policy implications can also be used by other emerging countries that would like to increase homeownership to provide better living conditions to citizens by making housing prices stable and keeping them under control. Hence, countries can control housing prices and stimulate housing affordability for citizens.