scholarly journals House price estimation using an eigenvector spatial filtering approach

2019 ◽  
Vol 13 (5) ◽  
pp. 845-867 ◽  
Author(s):  
Michael James McCord ◽  
John McCord ◽  
Peadar Thomas Davis ◽  
Martin Haran ◽  
Paul Bidanset
Keyword(s):  
Spatial Autocorrelation ◽  
House Prices ◽  
Spatial Scales ◽  
House Price ◽  
Spatial Filtering ◽  
Spatial Dependency ◽  
Content Type ◽  
Price Estimation ◽  
Eigenvector Spatial Filtering ◽  
Filtering Approach

Purpose Numerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an under-researched approach within house price studies. This paper aims to examine the spatial distribution of house prices using an eigenvector spatial filtering (ESF) procedure, to analyse the local variation and spatial heterogeneity. Design/methodology/approach Using 2,664 sale transactions over the one year period Q3 2017 to Q3 2018, an eigenvector spatial filtering approach is applied to evaluate spatial patterns within the Belfast housing market. This method consists of using geographical coordinates to specify eigenvectors across geographic distance to determine a set of spatial filters. These convey spatial structures representative of different spatial scales and units. The filters are incorporated as predictors into regression analyses to alleviate spatial autocorrelation. This approach is intuitive, given that detection of autocorrelation in specific filters and within the regression residuals can be markers for exclusion or inclusion criteria. Findings The findings show both robust and effective estimator consistency and limited spatial dependency – culminating in accurately specified hedonic pricing models. The findings show that the spatial component alone explains 14.6 per cent of the variation in property value, whereas 77.6 per cent of the variation could be attributed to an interaction between the structural characteristics and the local market geography expressed by the filters. This methodological step reduced short-scale spatial dependency and residual autocorrelation resulting in increased model stability and reduced misspecification error. Originality/value Eigenvector-based spatial filtering is a less known but suitable statistical protocol that can be used to analyse house price patterns taking into account spatial autocorrelation at varying (different) spatial scales. This approach arguably provides a more insightful analysis of house prices by removing spatial autocorrelation both objectively and subjectively to produce reliable, yet understandable, regression models, which do not suffer from traditional challenges of serial dependence or spatial mis-specification. This approach offers property researchers and policymakers an intuitive but comprehensible approach for producing accurate price estimation models, which can be readily interpreted.

scholarly journals A Multilevel Eigenvector Spatial Filtering Model of House Prices: A Case Study of House Sales in Fairfax County, Virginia

2019 ◽  
Vol 8 (11) ◽  
pp. 508
Author(s):  
Lan Hu ◽  
Yongwan Chun ◽  
Daniel A. Griffith
Keyword(s):  
Multilevel Model ◽  
House Prices ◽  
House Price ◽  
Spatial Filtering ◽  
Neighborhood Environment ◽  
Model Specification ◽  
Spatial Effects ◽  
Residential Areas ◽  
Fairfax County ◽  
Eigenvector Spatial Filtering

House prices tend to be spatially correlated due to similar physical features shared by neighboring houses and commonalities attributable to their neighborhood environment. A multilevel model is one of the methodologies that has been frequently adopted to address spatial effects in modeling house prices. Empirical studies show its capability in accounting for neighborhood specific spatial autocorrelation (SA) and analyzing potential factors related to house prices at both individual and neighborhood levels. However, a standard multilevel model specification only considers within-neighborhood SA, which refers to similar house prices within a given neighborhood, but neglects between-neighborhood SA, which refers to similar house prices for adjacent neighborhoods that can commonly exist in residential areas. This oversight may lead to unreliable inference results for covariates, and subsequently less accurate house price predictions. This study proposes to extend a multilevel model using Moran eigenvector spatial filtering (MESF) methodology. This proposed model can take into account simultaneously between-neighborhood SA with a set of Moran eigenvectors as well as potential within-neighborhood SA with a random effects term. An empirical analysis of 2016 and 2017 house prices in Fairfax County, Virginia, illustrates the capability of a multilevel MESF model specification in accounting for between-neighborhood SA present in data. A comparison of its model performance and house price prediction outcomes with conventional methodologies also indicates that the multilevel MESF model outperforms standard multilevel and hedonic models. With its simple and flexible feature, a multilevel MESF model can furnish an appealing and useful approach for understanding the underlying spatial distribution of house prices.

Using photographs and metadata to estimate house prices in South Korea

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Changro Lee ◽  
Key-Ho Park
Keyword(s):  
Neural Network ◽  
South Korea ◽  
Visual Information ◽  
House Prices ◽  
House Price ◽  
Content Type ◽  
Real Estate Valuation ◽  
Valuation Process ◽  
Price Estimation ◽  
The Impact

PurposeMost prior attempts at real estate valuation have focused on the use of metadata such as size and property age, neglecting the fact that the building workmanship in the construction of a house is also a key factor for the estimation of house prices. Building workmanship, such as exterior walls and floor tiling correspond to the visual attributes of a house, and it is difficult to capture and evaluate such attributes efficiently through classical models like regression analysis. Deep learning approach is taken in the valuation process to utilize this visual information.Design/methodology/approachThe authors propose a two-input neural network comprising a multilayer perceptron and a convolutional neural network that can utilize both metadata and the visual information from images of the front view of the house.FindingsThe authors applied the two-input neural network to Guri City in Gyeonggi Province, South Korea, as a case study and found that the accuracy of house price estimations can be improved by employing image information along with metadata.Originality/valueFew studies considered the impact of the building workmanship in the valuation process. The authors revealed that it is useful to use both photographs and metadata for enhancing the accuracy of house price estimation.

scholarly journals Liquidity, interest rates and house prices in the euro area: a DSGE analysis

2016 ◽  
Vol 9 (1) ◽  
pp. 4-25 ◽  
Author(s):  
Margarita Rubio ◽  
José A. Carrasco-Gallego
Keyword(s):  
Interest Rates ◽  
Monetary Union ◽  
Euro Area ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Dsge Model ◽  
Content Type ◽  
The Core ◽  
Dynamic Stochastic

Purpose This study aims to build a two-country monetary union dynamic stochastic general equilibrium (DSGE) model with housing to assess how different shocks contributed to the increase in housing prices and credit in the European Economic and Monetary Union. One of the countries is calibrated to represent the core group in the euro area, while the other one corresponds to the periphery. Design/methodology/approach In this paper, the authors explore how a liquidity shock (or a decrease in the interest rate) affects house prices and the real economy through the asset price and the collateral channel. Then, they analyze how a house price shock in the periphery and a technology shock in the core countries are transmitted to both economies. Findings The authors find that a combination of an increase in liquidity in the euro area coming from the common monetary policy, together with asymmetric house price and technology shocks, contributed to an increase in house prices in the euro area and a stronger credit growth in the peripheral economies. Originality/value This paper represents the theoretical counterpart to empirical studies that show, through macroeconometric models, the interrelation between liquidity and other shocks with house prices. Using a DSGE model with housing, the authors disentangle the mechanisms behind these empirical findings.

Rent or buy, what are the odds? Analysing the price-to-rent ratio for housing types within the Northern Ireland housing market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Daniel Lo ◽  
Michael James McCord ◽  
John McCord ◽  
Peadar Thomas Davis ◽  
Martin Haran
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Main Property ◽  
Property Market ◽  
Important Indicator ◽  
Content Type ◽  
Rental Market ◽  
Price Series ◽  
The Relationship

Purpose The price-to-rent ratio is often regarded as an important indicator for measuring housing market imbalance and inefficiency. A central question is the extent to which house prices and rents form part of the same market and thus whether they respond similarly to parallel stimulus. If they are close proxies dynamically, then this provides valuable market intelligence, particularly where causal relationships are evident. Therefore, this paper aims to examine the relationship between market and rental pricing to uncover the price switching dynamics of residential real estate property types and whether the deviation between market rents and prices are integrated over both the long- and short-term. Design/methodology/approach This paper uses cointegration, Wald exogeneity tests and Granger causality models to determine the existence, if any, of cointegration and lead-lag relationships between prices and rents within the Belfast property market, as well as the price-to-rent ratios amongst its five main property sub-markets over the time period M4, 2014 to M12 2018. Findings The findings provide some novel insights in relation to the pricing dynamics within Belfast. Housing and rental prices are cointegrated suggesting that they tend to move in tandem in the long run. It is further evident that in the short-run, the price series Granger-causes that of rents inferring that sales price information unidirectionally diffuse to the rental market. Further, the findings on price-to-rent ratios reveal that the detached sector appears to Granger-cause those of other property types except apartments in both the short- and long-term, suggesting possible spill-over of pricing signals from the top-end to the lower strata of the market. Originality/value The importance of understanding the relationship between house prices and rental market performance has gathered momentum. Although the house price-rent ratio is widely used as an indicator of over and undervaluation in the housing market, surprisingly little is known about the theoretical relationship between the price-rent ratio across property types and their respective inter-relationships.

scholarly journals House price cycles in emerging economies

2015 ◽  
Vol 32 (1) ◽  
pp. 17-52 ◽  
Author(s):  
Alessio Ciarlone
Keyword(s):  
Emerging Economies ◽  
House Prices ◽  
Small Body ◽  
House Price ◽  
Hedonic Pricing ◽  
Empirical Literature ◽  
Residential Property ◽  
Index Number ◽  
Price Dynamics ◽  
Content Type

Purpose – This paper aims to investigate the characteristics of house price dynamics for a sample of 16 emerging economies from Asia and Central and Eastern Europe over the period of 1995-2011. Design/methodology/approach – Linking housing valuations to a set of conventional fundamental determinants – relative to both the supply and the demand side of the market, institutional factors and other asset prices – and modelling short-term price dynamics – which reflect gradual adjustment to underlying fundamentals –conclusions about the existence and the basic nature of house price overvaluation (undervaluation) are drawn. Findings – Overall, it was found that actual house prices in the sample of emerging economies are not overly disconnected from fundamentals. Rather, they tend to reflect a somewhat slow adjustment to shocks to the latter. Moreover, the evidence that housing valuations may be driven by overly optimistic (or pessimistic) expectations is, in general, weak. Research limitations/implications – Residential property prices used in the empirical analysis have many limitations: while some series are derived using a hedonic pricing method, others are based on floor area prices collected by national authorities; while some countries publish house prices in national currency per-square metre (or per apartment or per dwelling), others calculate an index number scaled to some base year; while some countries publish statistics for the whole national territory, others produce data only for the capital city or for the largest cities in the country; data from national sources refer to different types of residential property; finally, available time series are relatively short, which may adversely affect the robustness of estimation results. Practical implications – The decomposition suggested in the paper has important implications: it would be paramount, in fact, for policymakers to implement market-specific diagnoses, and to find the right policy instruments that can ideally distinguish between the two underlying components driving house price short-run dynamics. Originality/value – There is a very small body of empirical literature on housing market developments in emerging economies, especially if focussed on the comparisons between the actual dynamics of housing valuations and the equilibrium ones.

House prices and credit cycles: the case of Cyprus

2020 ◽  
Vol 38 (6) ◽  
pp. 539-550
Author(s):  
Dario Pontiggia ◽  
Petros Stavrou Sivitanides
Keyword(s):  
Financial Crisis ◽  
Global Financial Crisis ◽  
House Prices ◽  
House Price ◽  
Equation Model ◽  
Housing Demand ◽  
Content Type ◽  
Bank Deposits ◽  
The Global Financial Crisis ◽  
Practical Implications

PurposeThe purpose of this paper is to assess whether the rapid accumulation of bank deposits before the global financial crisis and their subsequent drastic reduction was the main driving force of the Cyprus house price cycle over the period 2006–2015.Design/methodology/approachTo this aim we estimate a three-equation model in which house prices are determined by housing loans, among other factors, and housing loans are determined by bank deposits. All equations are estimated using partial adjustment model specifications.FindingsOur findings indicate that housing loans, which capture the effect of credit availability on housing demand, had the smallest effect on house prices, thus providing little support to our proposition of a deposits-driven cycle in house prices.Research limitations/implicationsThe main limitation of the study is the use of the housing loan stock instead of the actual volume of housing loans in each period due to lack of such data. As a result our econometric estimates may not accurately capture the magnitude of the effect of housing loans on house prices.Practical implicationsThe study has important practical implications for policy makers as it highlights the importance of availability of credit in supporting effective demand for housing during periods of economic growth. Furthermore, it highlights the key role of house price increases in combination with the collateral effect in driving the house price cycle.Originality/valueThis is among the few studies internationally and the first study in Cyprus that attempts to link econometrically the credit and house price cycles that were caused by the global financial crisis.

The Internet reshapes China's economic geography: micromechanisms and macro effects

2020 ◽  
Vol 3 (2) ◽  
pp. 259-283
Author(s):  
Chen Yang ◽  
Tongliang An
Keyword(s):  
Economic Geography ◽  
House Prices ◽  
House Price ◽  
The Internet ◽  
Dispersion Force ◽  
Content Type ◽  
The Real Estate ◽  
Real Estate Sector ◽  
Spatial Panel Data ◽  
The Impact

PurposeBy observing facts of the “reversal of agglomeration” of Chinese enterprises during the period of rapid Internet development and using a new economic geography model combined with the data of the real estate sector, this paper deduces the influence of the “reshaping mechanisms” of the Internet on China's economic geography based on the “gravitation mechanism” of the Internet that affects the enterprises and the “amplification mechanism” of the Internet that amplifies the dispersion force of house prices.Design/methodology/approachIn the empirical aspect, the dynamic spatial panel data model is used to test the micromechanisms of the impact of the Internet on enterprises' choice of location and the instrumental variable method is used to verify the macro effects of the Internet in reshaping economic geography.FindingsIt is found that in the era of the network economy, the Internet has become a source of regional competitive advantage and is extremely attractive to enterprises. The rapidly rising house price has greatly increased the congestion cost and has become the force behind the dispersion of enterprises. China's infrastructure miracle has closed the access gap which gives full play to network externalities and promotes the movement of enterprises from areas with high house prices to areas with low house prices.Originality/valueThe Internet is amplifying the dispersion force of congestion costs manifested as house prices and is reshaping China's economic geography. This paper further proposes policy suggestions such as taking the Internet economy as the new momentum of China's economic development and implementing the strategy of regional coordinated development.

scholarly journals A dynamic model for housing price spillovers with an evidence from the US and the UK markets

2018 ◽  
Vol 2 (1) ◽  
pp. 70-81 ◽  
Author(s):  
Alper Ozun ◽  
Hasan Murat Ertugrul ◽  
Yener Coskun
Keyword(s):  
New York ◽  
Real Estate ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Housing Markets ◽  
Content Type ◽  
Real Estate Markets ◽  
Country Level ◽  
The Us

Purpose The purpose of this paper is to introduce an empirical model for house price spillovers between real estate markets. The model is presented by using data from the US-UK and London-New York housing markets over a period of 1975Q1-2016Q1 by employing both static and dynamic methodologies. Design/methodology/approach The research analyzes long-run static and dynamic spillover elasticity coefficients by employing three methods, namely, autoregressive distributed lag, the fully modified ordinary least square and dynamic ordinary least squares estimator under a Kalman filter approach. The empirical method also investigates dynamic correlation between the house prices by employing the dynamic control correlation method. Findings The paper shows how a dynamic spillover pricing analysis can be applied between real estate markets. On the empirical side, the results show that country-level causality in housing prices is running from the USA to UK, whereas city-level causality is running from London to New York. The model outcomes suggest that real estate portfolios involving US and UK assets require a dynamic risk management approach. Research limitations/implications One of the findings is that the dynamic conditional correlation between the US and the UK housing prices is broken during the crisis period. The paper does not discuss the reasons for that break, which requires further empirical tests by applying Markov switching regime shifts. The timing of the causality between the house prices is not empirically tested. It can be examined empirically by applying methods such as wavelets. Practical implications The authors observed a unidirectional causality from London to New York house prices, which is opposite to the aggregate country-level causality direction. This supports London’s specific power in the real estate markets. London has a leading role in the global urban economies residential housing markets and the behavior of its housing prices has a statistically significant causality impact on the house prices of New York City. Social implications The house price co-integration observed in this research at both country and city levels should be interpreted as a continuity of real estate and financial integration in practice. Originality/value The paper is the first research which applies a dynamic spillover analysis to examine the causality between housing prices in real estate markets. It also provides a long-term empirical evidence for a dynamic causal relationship for the global housing markets.

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