Housing price volatility and its determinants

Lee, Chyi Lin

doi:10.1108/17538270910977572

Cited by 59 publications

(30 citation statements)

References 43 publications

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“…The increasing population causes the variation of the supply-demand relationship of the real estate market and affects the property prices. Lee (2009) demonstrated that the volatility of population is influential in affecting the fluctuation of housing prices. The past high volatility of population will cause an amplification effect for the current fluctuation of housing market.…”

Section: Data Collectionmentioning

confidence: 99%

Forecasting Property Price Indices in Hong Kong Based on Grey Models

Tan

Hui

2017

International Journal of Strategic Property Management

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The real estate market in Hong Kong plays an important role in its economy. The property prices have been increasing a lot since 2009, which have become a major concern. However, few studies have been done to forecast the property price indices in Hong Kong. In this paper, two grey models, GM(1,1) and GM(0,N), are introduced for the forecasting. The results show that GM(1,1) has a better performance when forecasting with stable trend data, while GM(0,N) is more suitable for forecasting data in fluctuating trend. The sensitivity analysis for GM(0,N) shows that Population(POP) and Best Lending Rate(BLR) are significantly sensitive factors for data in stable trend. While for the fluctuating data, sensitivity of each factor presents uncertainties. This study also compares the forecasting performance of grey models with the ANN model and ARIMA model. The study demonstrates that grey models are more suitable for forecasting the Hong Kong property price indices than others.

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Section: Data Collectionmentioning

confidence: 99%

Forecasting Property Price Indices in Hong Kong Based on Grey Models

Tan

Hui

2017

International Journal of Strategic Property Management

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“…The UK house price volatility investigation consists of the work of Willcocks (2010), Tsai, Chen, and Ma (2010), Milles (2011b), and more recently, Begiazi and Katsiampa (2019). The Australian house price volatility has been examined by Lee (2009) and Lee and Reed (2014b); while Hossain and Latif (2009) and Lin and Fuerst (2014) studied the Canadian house price volatility. For Finland, Dufitinema (2020) has recently explored different aspects of the Finnish housing market volatility.…”

Section: Introductionmentioning

confidence: 99%

Stochastic volatility forecasting of the Finnish housing market

Dufitinema

2020

Applied Economics

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The purpose of the article is to assess the in-sample fit and the out-of-sample forecasting performances of four stochastic volatility (SV) models in the Finnish housing market. The competing models are the vanilla SV, the SV model where the latent volatility follows a stationary AR(2) process, the heavy-tailed SV and the SV with leverage effects. The models are estimated using Bayesian technique, and the results reveal that the SV with leverage effects is the best model for modelling the Finnish house price volatility. The heavy-tailed SV model provides accurate out-ofsample volatility forecasts in most of the studied regions. Additionally, the models' performances are noted to vary across almost all cities and sub-areas, and by apartment types. Moreover, the AR (2) component substantially improves the in-sample fit of the standard SV, but it is unimportant for the out-of-sample forecasting performance. The study outcomes have crucial implications, such as portfolio management and investment decision-making. To establish suitable time-series volatility forecasting models of this housing market, these study outcomes will be compared to the performances of their GARCH models counterparts.

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“…The development of the housing market is determined by many closely related economic, legal, financial, institutional and political factors [1]. They can be quantified primarily in international terms, where macroeconomic factors such as GDP (gross domestic product), inflation, rates of return and availability of mortgage loans play a major role [2][3][4]. There are several factors that affect demand throughout the country, generally regardless of local variations.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression

Cellmer

Cichulska

Bełej

2020

IJGI

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The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis.

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Housing price volatility and its determinants

Cited by 59 publications

References 43 publications

Forecasting Property Price Indices in Hong Kong Based on Grey Models

Forecasting Property Price Indices in Hong Kong Based on Grey Models

Stochastic volatility forecasting of the Finnish housing market

Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression

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