Spatial–temporal neighbourhood-level house price index

Purpose The purpose of this study is to develop a spatio-temporal neighbourhood-level house price index (STNL-HPI) incorporating a geographic information system (GIS) functionality that can be used to improve the house price indexation system. Design/methodology/approach By using the Malaysian house price index (MHPI) and application of geographically weighted regression (GWR), GIS-based analysis of STNL-HPI through an application called LHPI Viewer v.1.0.0, the stand-alone GIS-statistical application for STNL-HPI was successfully developed in this study. Findings The overall results have shown that the modelling and GIS application were able to help users understand the visual variation of house prices across a particular neighbourhood. Research limitations/implications This research was only able to acquire data from the federal government over the period 1999 to 2006 because of budget limitations. Data purchase was extremely costly. Because of financial constraints, data with lower levels of accuracy have been obtained from other sources. As a consequence, a major portion of data was mismatched because of the absence of a common parcel identifier, which also affected the comparison of this system to other comparable systems. Originality/value Neighbourhood-level HPI is needed for a better understanding of the local housing market.

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Long-term regional house prices cycles. A city-based index for Italy

Journal of European Real Estate Research ◽

10.1108/jerer-05-2017-0019 ◽

2017 ◽

Vol 10 (3) ◽

pp. 303-330 ◽

Cited By ~ 3

Author(s):

Laura Gabrielli ◽

Paloma Taltavull de La Paz ◽

Armando Ortuño Padilla

Keyword(s):

Time Series ◽

Price Index ◽

House Prices ◽

Housing Prices ◽

House Price ◽

City Centre ◽

House Price Index ◽

Content Type ◽

The Common

Purpose This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data. Design/methodology/approach This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals. Findings Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level. Research limitations/implications Data are measured as the average price in squared meters, and the resulting index is not quality controlled. Practical implications The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices. Originality/value This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.

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House price index (HPI) and Covid-19 pandemic shocks: evidence from Turkey and Kazakhstan

International Journal of Housing Markets and Analysis ◽

10.1108/ijhma-10-2020-0126 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yeşim Aliefendioğlu ◽

Harun Tanrivermis ◽

Monsurat Ayojimi Salami

Keyword(s):

Real Estate ◽

Price Index ◽

Housing Prices ◽

House Price ◽

Monthly Basis ◽

Data Set ◽

House Price Index ◽

Content Type ◽

Asymmetric Pricing ◽

Long Run

Purpose This paper aims to investigate asymmetric pricing behaviour and impact of coronavirus (Covid-19) pandemic shocks on house price index (HPI) of Turkey and Kazakhstan. Design/methodology/approach Monthly HPIs and consumer price index (CPI) data ranges from 2010M1 to 2020M5 are used. This study uses a nonlinear autoregressive distributed lag model for empirical analysis. Findings The findings of this study reveal that the Covid-19 pandemic exerted both long-run and short-run asymmetric relationship on HPI of Turkey while in Kazakhstan, the long-run impact of Covid-19 pandemic shock is symmetrical long-run positive effect is similar in both HPI markets. Research limitations/implications The main limitations of this study are the study scope and data set due to data constraint. Several other macroeconomic variables may affect housing prices; however, variables used in this study satisfy the focus of this study in the presence of data constraint. HPI and CPI variables were made available on monthly basis for a considerably longer period which guaranteed the ranges of data set used in this study. Practical implications Despite the limitation, this study provides necessary information for authorities and prospective investors in HPI to make a sound investment decision. Originality/value This is the first study that rigorously and simultaneously examines the pricing behaviour of Turkey and Kazakhstan HPIs in relation to the Covid-19 pandemic shocks at the regional level. HPI of Kazakhstan is recognized in the global real estate transparency index but the study is rare. The study contributes to regional studies on housing price by bridging this gap in the real estate literature.

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A hedonic house price index in continuous time

International Journal of Housing Markets and Analysis ◽

10.1108/ijhma-10-2015-0066 ◽

2016 ◽

Vol 9 (4) ◽

pp. 648-670 ◽

Cited By ~ 7

Author(s):

Sofie R. Waltl

Keyword(s):

Price Index ◽

House Price ◽

Additive Model ◽

Housing Markets ◽

Smooth Functions ◽

House Price Index ◽

Content Type ◽

Monitoring Tools ◽

Continuous Index ◽

Sydney Australia

Purpose This paper aims to develop a methodology to accurately and timely measure movements in housing markets by constructing a continuously estimated house price index. Design/methodology/approach The continuous index, which is extracted from an additive model that includes the temporal and the locational effects as smooth functions, can be interpreted as an extension of the classical hedonic time-dummy method. The methodology is applied to housing sales from Sydney, Australia, between 2001 and 2011, and compared to three types of discrete indexes. Findings Discrete indexes turn out to approach the continuously estimated index with decreasing period lengths but eventually become wiggly and unreliable because of fewer observations per period. The continuous index, in contrast, is stable, has favourable robustness properties and is more objective in several ways. Originality/value The resulting index tracks movements in the housing market precisely and in “real-time” and is hence suited for monitoring and assessing housing markets. Because turbulence in housing markets is often a harbinger of financial crises, such monitoring tools support policymakers and investors in tailoring their decisions and reactions. Additionally, the index can be evaluated arbitrarily frequently and therefore is well suited for use in property derivatives.

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House price overvaluation in Hong Kong

International Journal of Housing Markets and Analysis ◽

10.1108/ijhma-01-2016-0003 ◽

2017 ◽

Vol 10 (2) ◽

pp. 282-304 ◽

Cited By ~ 6

Author(s):

Philip Arestis ◽

Ana Rosa Gonzalez-Martinez ◽

Lu-kui Jia

Keyword(s):

Hong Kong ◽

Housing Market ◽

Design Methodology ◽

House Prices ◽

House Price ◽

Theoretical Framework ◽

Second Step ◽

Content Type

Purpose The purpose of this paper is twofold. First, the authors investigate the main drivers of house prices in the Hong Kong housing market. Second, further research is undertaken to confirm the existence of house price overvaluation, which has driven the market into a bubble episode. Design/methodology/approach First, the authors propose a theoretical framework to identify the fundamentals of the market. In the second step, they decompose house prices into fundamentals, frictions and bubble episodes for a better understanding of the evolution of house prices during the period 1996(Q3)-2013(Q3). Findings The results of this paper suggest an eventual possible correction of up to 46 per cent of house prices with respect to their 2013(Q3) level. Originality/value The originality of this paper is to use the procedure developed by Glindro and Delloro (2010) to analyse the Hong Kong housing market. The contribution of this paper also modifies the original Glindro and Delloro’s (2010) approach by including the Christiano and Fitzgerald (2003) filter to decompose house prices.

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Understanding the short- and long-run relationship between vacant allotment and established house prices

International Journal of Managerial Finance ◽

10.1108/ijmf-04-2012-0052 ◽

2014 ◽

Vol 10 (2) ◽

pp. 200-217 ◽

Cited By ~ 1

Author(s):

Peter Rossini ◽

Valerie Kupke

Keyword(s):

Price Index ◽

House Prices ◽

House Price ◽

Content Type ◽

Land Prices ◽

Long Run ◽

Short Run ◽

A Site ◽

Urban Land Market

Purpose – The purpose of this paper is to address a key issue fundamental to the operation of land and housing markets, that is, the relationship between land and house prices. The study identifies possible causation between established house and vacant allotment prices using the metropolitan area of Adelaide, Australia as a case study. Design/methodology/approach – A key outcome of the study is the construction of a Site Adjusted Land Price Index against which a Quality Adjusted House Price Index is compared. Findings – The results show that there is a lagged effect of land prices on house prices and that this is significant at an interval of eight lag periods. The results also imply that the lead lag relationship between established house and vacant allotment prices is not unidirectional. This suggests that, while a change in house prices leads to a change in land prices in the short-run, the long-run position is for increasing land prices to lead to a delayed increase in house prices. Research limitations/implications – Rising house prices do not simply and solely reflect a shortage of land. There are suggested effects both immediate from house to land and delayed from land to house, particularly in a rising market. Originality/value – The lead lag relationships of both indexes are tested using Granger causality estimates to assess whether theoretical Ricardian concepts still hold in a modern urban land market.

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An investigation of the synchronization in global house prices

Journal of European Real Estate Research ◽

10.1108/jerer-12-2019-0050 ◽

2020 ◽

Vol 13 (1) ◽

pp. 17-27 ◽

Cited By ~ 1

Author(s):

Martin Hoesli

Keyword(s):

Design Methodology ◽

House Prices ◽

House Price ◽

Housing Markets ◽

Contributing Factors ◽

Policy Makers ◽

Demand And Supply ◽

Content Type ◽

House Price Indices ◽

Practical Implications

Purpose The purpose of this paper to provide a discussion of the empirical evidence and contributing factors of the synchronization of house prices globally. Design/methodology/approach The author reviewed the main studies on house price synchronization and conducted an empirical analysis using OECD house price indices. A discussion of the contributing factors of synchronization, with a focus on the demand and supply dimensions is provided, and synchronization across both countries and cities is examined. Findings Housing markets globally have become more synchronized; this is particularly clear for cities. The sustained demand for places that are attractive for financial motives and for lifestyle and sometimes climate along with the fact that such places tend to be supply-constrained is likely to lead to more synchronization across markets. Practical implications The conclusions are important for investors seeking to diversify their housing holdings internationally. The discussion should also benefit policy-makers. Originality/value To date, very scarce evidence exists on the synchronization of house prices globally. By surveying the results contained in previous studies and providing a thorough discussion of the possible drivers of house price synchronization, this study contributes to a better understanding of this important topic.

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The resilience and realignment of house prices in the era of Covid-19*

Journal of European Real Estate Research ◽

10.1108/jerer-11-2020-0055 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

John V. Duca ◽

Martin Hoesli ◽

Joaquim Montezuma

Keyword(s):

Design Methodology ◽

House Prices ◽

House Price ◽

Behavioral Factors ◽

Content Type ◽

Downward Pressure ◽

Price Indexes ◽

Price Appreciation ◽

The Usa ◽

Rate Environment

Purpose The study aims to analyze the effects of the Covid-19 pandemic on house prices. Design/methodology/approach The authors start by discussing the possibility that house price indexes may not fully incorporate the effects of the pandemic as of yet. Against the background of the pandemic, the authors then analyze economic and behavioral effects affecting house prices. The authors also discuss how the linkages between tourism and house prices have been affected. The authors further present evidence of an emerging shift in preferences from urban locations to more peripheral ones. Findings The authors report variance in the evolution of house prices across countries at the onset of the pandemic, with locations depending heavily on tourism showing slower price appreciation while appreciation has firmed in other places. The authors argue that the resilience of house prices is not only because of the low-interest rate environment and government efforts to support firms and households, but also behavioral factors. In some locations, the price of condominiums has declined relative to the price of detached houses. This could indicate that wealthier households are seeking more space and larger units as a result of the crisis. There is also evidence of a downward pressure on rents, leading to increased price–rent ratios in the USA. Originality/value By considering both economic and behavioral factors, this paper provides for a better understanding of the resilience and realignment of house prices at the onset of the Covid-19 pandemic.

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Prediction of House Price Index Based on Bagging Integrated WOA-SVR Model

Mathematical Problems in Engineering ◽

10.1155/2021/3744320 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Xiang Wang ◽

Shen Gao ◽

Shiyu Zhou ◽

Yibin Guo ◽

Yonghui Duan ◽

...

Keyword(s):

Prediction Accuracy ◽

Price Index ◽

House Prices ◽

House Price ◽

Support Vector ◽

Data Set ◽

Support Vector Regression Model ◽

House Price Index ◽

Penalty Factor ◽

Whale Optimization

Aiming at the shortcomings of a single machine learning model with low model prediction accuracy and insufficient generalization ability in house price index prediction, a whale algorithm optimized support vector regression model based on bagging ensemble learning method is proposed. Firstly, gray correlation analysis is used to obtain the main influencing factors of house prices, and the segmentation forecasting method is used to divide the data set and forecast the house prices in the coming year using the data of the past ten years. Secondly, the whale optimization algorithm is used to find the optimal parameters of the penalty factor and kernel function in the SVR model, and then, the WOA-SVR model is established. Finally, in order to further improve the model generalization capability, a bagging integration strategy is used to further integrate and optimize the WOA-SVR model. The experiments are conducted to forecast the house price indices of four regions, Beijing, Shanghai, Tianjin, and Chongqing, respectively, and the results show that the prediction accuracy of the proposed integrated model is better than the comparison model in all cases.

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