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.

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.

LIBOR, base rate spreads and the UK housing market

2015 ◽  
Vol 8 (1) ◽  
pp. 118-134 ◽  
Author(s):  
Martin Hinch ◽  
Jim Berry ◽  
William McGreal ◽  
Terry Grissom
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
House Prices ◽  
House Price ◽  
Base Rate ◽  
Percentage Change ◽  
Content Type ◽  
Change Models ◽  
The Uk ◽  
The Impact

Purpose – The purpose of this paper is to analyse how London Interbank Offered Rate Index (LIBOR) and the spread between LIBOR and the base rate of interest as set by the Bank of England (BoE) influences the variation in house prices in the UK. Design/methodology/approach – This paper uses monthly data over a long time series, since 1986, to investigate the relationships between house price and LIBOR. Data are drawn from several different sources to include housing, financial and macro-economic variables. The time series is sub-divided into a series of splines based on stages in the economic and property market cycle. Both value-based and percentage change models are developed. Findings – The results show that BoE base/LIBOR margin variable has a strong positive and significant effect on house price; however, the percentage change model infers a weaker and inverse relationship. The spline analysis re-emphasised the significance of the BoE base/LIBOR margin variable. Where variation between base rates and LIBOR is reduced, a significant positive effect can be observed in the average house price; however, where significant variation exists, the BoE base/LIBOR margin has little effect and LIBOR itself becomes a significant driver. Research limitations/implications – The results highlight that the predictive qualities of the BoE base/LIBOR margin, as the contribution of this margin to the explanation of house price, exceeds both the base rate and LIBOR variables individually. Also highlighted is the contribution of unemployment to the explanation of house price. In both the value and percentage change models, unemployment is shown as a negative and highly significant contributor. Originality/value – Previous papers have demonstrated the important linkage between house price and interest rates, the originality in this paper lies in examining the impact of LIBOR and the spreads between LIBOR and base rate as key variables influencing variation in UK house prices.

scholarly journals House prices and neighbourhood amenities: beyond the norm?

2018 ◽  
Vol 11 (2) ◽  
pp. 263-289 ◽  
Author(s):  
Michael James McCord ◽  
Peadar Thomas Davis ◽  
Paul Bidanset ◽  
William McCluskey ◽  
John McCord ◽  
...  
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Ordinary Least Squares ◽  
Content Type ◽  
Conditional Quantiles ◽  
Price Distribution ◽  
Regression Techniques ◽  
High Level ◽  
The Impact

Purpose Understanding the key locational and neighbourhood determinants and their accessibility is a topic of great interest to policymakers, planners and property valuers. In Northern Ireland, the high level of market segregation means that it is problematic to understand the nature of the relationship between house prices and the accessibility to services and prominent neighbourhood landmarks and amenities. Therefore, this paper aims to quantify and measure the (dis)amenity effects on house pricing levels within particular geographic housing sub-markets. Design/methodology/approach Most hedonic models are estimated using regression techniques which produce one coefficient for the entirety of the pricing distribution, culminating in a single marginal implicit price. This paper uses a quantile regression (QR) approach that provides a “more complete” depiction of the marginal impacts for different quantiles of the price distribution using sales data obtained from 3,780 house sales transactions within the Belfast Housing market over 2014. Findings The findings emerging from this research demonstrate that housing and market characteristics are valued differently across the quantile values and that conditional quantiles are asymmetrical. Pertinently, the findings demonstrate that ordinary least squares (OLS) coefficient estimates have a tendency to over or under specify the marginal mean conditional pricing effects because of their inability to adequately capture and comprehend the complex spatial relationships which exist across the pricing distribution. Originality value Numerous studies have used OLS regression to measure the impact of key housing market externalities on house prices, providing a single estimate. This paper uses a QR approach to examine the impact of local amenities on house prices across the house price distribution.

Causal effects of immigration inflows on house prices in a traditionally non-destination country

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Woei Chyuan Wong ◽  
Jan-Jan Soon
Keyword(s):  
Fixed Effects ◽  
Positive Impact ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Developed Countries ◽  
Annual Data ◽  
Content Type ◽  
Variable Approach ◽  
The Impact

Purpose The purpose of this study is to examine the causal impact of international immigration inflows on housing prices at the state level in Malaysia from 2007 to 2018. Design/methodology/approach Hedonic regressions using both fixed effects and first difference approaches are used to estimate the impact of immigration inflows on house prices in Malaysia. This study deals with potential endogeneity of immigrants’ choices of destination states in Malaysia by using a shift-share instrument variable approach. Specifically, historical shares of immigrants in a state are used to predict current immigrant inflows to a particular state. The predicted value of immigration flows is then inserted into the house price regression models in place of the actual immigration flows. Findings Using annual data for 14 states from 2007 to 2018, this study documents the positive impact of immigration inflows on house prices in Malaysia. The authors find that a 1% increase in immigration inflows is associated with an increase of 10.2% (first difference) and 13.4% (fixed effects) in house prices. The economic impact is larger in magnitude than that found in developed countries. Contrary to existing studies that find immigration inflows to be associated with native flight, the authors find support for the attraction effects hypothesis, where immigration inflow is positive and significantly related to net native flows. Research limitations/implications The effects of immigration inflows are economically significant, considering that the effects are 10 times larger than those documented in the USA. Policymakers in Malaysia ought to monitor house price trends in immigrant-popular states to ensure that natives are not priced out by new immigrants. Originality/value To the best of the authors’ knowledge, this is perhaps the first study to focus on the relationship between immigration inflows and house prices in Malaysia. Focusing on Malaysia has at least two originality aspects. First, Malaysia is relatively not an immigrant-popular destination. Second, Malaysia has a multiracial and heterogenous society among its natives. The findings, obtained within these two settings, would therefore provide a wider scope of result generalization, and natural experiment grounds for causal implications of our results.

The potential impact of Covid-19 on the Irish housing sector

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Matthew Allen-Coghlan ◽  
Kieran Michael McQuinn
Keyword(s):  
House Prices ◽  
House Price ◽  
Mortgage Market ◽  
Public Authorities ◽  
Content Type ◽  
Market Activity ◽  
Household Disposable Income ◽  
Near Term ◽  
Residential Market ◽  
The Impact

Purpose This paper aims to examine the implications for the Irish housing market of the economic slowdown due to the Covid-19 virus. Design/methodology/approach In this paper, an inverted demand function for housing is augmented to include a residential market activity variable and estimate the impact on house prices of the decline in economic activity due to the virus-related measures. The likely future path of house prices based on two different recovery scenarios is also examined. Under both scenarios house prices are forecast to decline in the near term. Findings The scenario analysis presented here indicates that Irish house prices are set to fall over the next 18 months as a result of the Covid-19 downturn. This contraction in prices is due to the decline in household disposable income and the sharp fall-off in mortgage market activity, which will inevitably result from the administrative closedown implemented by the Irish authorities. Originality/value As such the approach builds on several studies which have examined both house price movements in general and the relationship between house prices and mortgage credit availability. The paper also draws on the latest analysis of the implications for the Irish economy of Covid-19 and the related administrative closure methods introduced by the public authorities.

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 Impact of crime on spatial analysis of house prices: evidence from a UK city

2016 ◽  
Vol 9 (4) ◽  
pp. 627-647 ◽  
Author(s):  
David McIlhatton ◽  
William McGreal ◽  
Paloma Taltavul de la Paz ◽  
Alastair Adair
Keyword(s):  
Spatial Analysis ◽  
House Prices ◽  
Spatial Association ◽  
House Price ◽  
Spatial Effects ◽  
Hedonic Price ◽  
Spatial Autoregression ◽  
Spatial Error ◽  
Content Type ◽  
The Impact

Purpose There is a lack of understanding in the literature on the spatial relationships between crime and house price. This paper aims to test the impact of spatial effects in the housing market, how these are related to the incidence of crime and whether effects vary by the type of crime. Design/methodology/approach The analysis initially explores univariate and bivariate spatial patterns in crime and house price data for the Belfast Metropolitan Area using Moran’s I and Local Indicator Spatial Association (LISA) models, and secondly uses spatial autoregression models to estimate the role of crime on house prices. A spatially weighted two-stage least-squares model is specified to analyse the joint impact of crime variables. The analysis is cross sectional, based on a panel of data. Findings The paper illustrates that the pricing impact of crime is complex and varies by type of crime, property type and location. It is shown that burglary and theft are associated with higher-income neighbourhoods, whereas violence against persons, criminal damage and drugs offences are mainly associated with lower-priced neighbourhoods. Spatial error effects are reduced in models based on specific crime variables. Originality/value The originality of this paper is the application of spatial analysis in the study of the impact of crime upon house prices. Criticisms of hedonic price models are based on unexplained error effects; the significance of this paper is the reduction of spatial error effects achievable through the analysis of crime data.

scholarly journals Making sense of increased synchronization in global house prices

2020 ◽  
Vol 13 (1) ◽  
pp. 5-16
Author(s):  
John V. Duca
Keyword(s):  
Real Estate ◽  
Interest Rates ◽  
House Prices ◽  
House Price ◽  
Substitution Effects ◽  
Content Type ◽  
International Portfolio Diversification ◽  
Gateway Cities ◽  
The Impact ◽  
International House

Purpose The purpose of this paper is to provide perspective on whether and why global metro house prices have become more synchronized, and perspective on the limited implications of this for investing in international real estate. Design/methodology/approach This paper reviews main findings from the literature on house price determination, reviews the emerging literature on global synchronization, and provides graphs to illustrate main points and trends. Findings House prices have become somewhat more synchronized likely reflecting greater correlation in long-term interest rates and macroeconomic cycles related to trends in globalization and international portfolio diversification. Nevertheless, this trend has not been continuous, reflecting that house prices depend on other fundamentals, which are not uniform across areas. Theory and evidence indicate that the more common are fundamentals, the more synchronized are house price cycles and the more substitution effects may matter. Also, real estate markets that are open to immigration and foreign investment have become more sensitive to shifts in the international demand for property by migrants or investors. Research limitations/implications Changes in international house price synchronization stem from variation in two categories of key drivers of house prices. The first are traditional supply and demand fundamentals. The second include international capital flows and immigration. Both sets of factors are sensitive to the economic environment and public policy. Increased synchronization of business cycles, the Euro currency union, and more common monetary policy strategies and tactics have fostered greater correlation of real interest rates across countries, which tend to increase house price synchronization. These effects can be amplified by the tendency for property owners to use extrapolative expectations of future house prices. Practical implications Shifts in prospective returns and the synchronization of international property returns not only on arbitrage of general property price differentials but also on underlying factors driving those differentials. Investors need to be mindful of the risks that metro prices sometimes reflect bubble-builder dynamics that can give rise to over-shooting of house prices. Observing simple correlations and changes in those correlations does not do away with the need for careful analysis of property investment, and if anything, warrant analysis of both how and why one may observe changes in the extent to which international house prices is synchronized. Social implications Despite the rise of globalization and of new technologies, the author has seen substantial divergences in house prices emerge across gateway cities and metros in less vibrant areas within countries. These reflect not only the impact of stronger income and population in more tech, educated and global oriented cities but also changes in the demand for amenities toward more culturally appealing cities, often – but not exclusively in – warmer or coastal areas where the supply elasticity of housing is often limited. Further complicating investment decisions are potential shifts in housing or immigration policy that can notably affect the demand for housing. Originality/value The paper provides practical perspective on why different groups of international cities have seen their house prices become more sychronized. Nevertheless, increased synchronization has occurred within an elite set of major cities, but in an environment house prices have diverged across gateway cities and metros in less vibrant areas within countries. The paper helps investors make sense of some recent patterns and recent prospects for investing in international real estate.

scholarly journals Housing prices in peninsular Malaysia: supported by income, foreign inflow or speculation?

2019 ◽  
Vol 12 (3) ◽  
pp. 424-441 ◽  
Author(s):  
Wang Li Wong ◽  
Chin Lee ◽  
Seow Shin Koong
Keyword(s):  
Housing Market ◽  
Low Income ◽  
House Prices ◽  
Correction Term ◽  
Housing Prices ◽  
House Price ◽  
Peninsular Malaysia ◽  
Financial Asset ◽  
Content Type ◽  
The Impact

Purpose This paper is motivated by a concern about the ability of the average Malaysian income to catch up with the rapidly increasing house prices in Peninsular Malaysia. Financial innovation in financial system now regards houses as a financial asset and speculation vehicle. Therefore, a house purchase is made to acquire not merely a necessity but also a financial asset which can generate future returns. Given the problems in the housing market, this paper aims to examine the determinants of house prices in Malaysia, including those such as income, population, foreign inflow and speculation. Design/methodology/approach This study adopts panel data analyses, namely, the fixed effect model (FEM) and the pooled mean group (PMG), and uses data at state level in quarterly frequency, spanning from 2005Q1 to 2013Q4. Findings Based on the results of FEM, these determinants influence house prices significantly. Moreover, the PMG results suggest that there is convergence in the model, which are indicated by the significant and negative sign of the error correction term. In conclusion, the rapidly increasing house price is not caused by speculation activities in the housing market. More precisely, Malaysian income is capable of catching up with the increasing house prices. Practical implications As income remains to be one of the major drivers in influencing Malaysian house price, Malaysian Government shall continue the policies of supply low cost houses to the low-income groups and My First Home Scheme (SRP) by offering less stringent rules in applying house loan for the first-time house buyers. Originality/value This study used the actual data of foreign housing purchase obtained from Malaysia Valuation and Property Services Department to represent foreign inflow; therefore, the results will reflect the impact of foreign inflow in a better manner.

scholarly journals Effects of ethnic changes on house prices: Sydney cases

2018 ◽  
Vol 13 (1) ◽  
pp. 96-119 ◽  
Author(s):  
Xin Janet Ge
Keyword(s):  
Census Data ◽  
House Prices ◽  
House Price ◽  
Policy Formulation ◽  
Pilot Studies ◽  
Content Type ◽  
Australian Bureau Of Statistics ◽  
Ethnic Factors ◽  
The Impact ◽  
Multicultural Communities

Purpose This paper aims to investigate the factors that contribute to the changes of house prices including ethnic factors. Australia is a multicultural country with diversified ethnicities. The median price of established houses (unstratified) in Sydney has reached a new record high of $910,000 in December 2015, increasing around 58.2 per cent from March 2011 [Australian Bureau of Statistics (ABS), 2015a]. However, the prices of some suburbs have increased more than prices of others. Design/methodology/approach Six suburbs that represent ethnic majority originally including White, India and China will be selected as pilot studies. Hedonic regression analysis will be applied for the analysis based on 2001, 2006 and 2011 census data. Findings It is found that the main drivers of house prices are the dwelling physical characteristics and accessibility to convenient transportation. The level of household income also plays an important role. However, the impact of changes of ethnic on changes of prices is not significant. Research limitations/implications The study adds to the growing literature on the ethnicity changes on dwelling prices and is important for understanding whether some of the clusters of ethnic concentration or segregation effects property markets. This study is significant in its understanding of the main characteristics of ethnic changes of suburbs in Sydney. Practical implications An implication is that policy makers can attract different ethnic groups and encourage multicultural communities when they formulate housing and planning policies. Originality/value The relationship between ethnicity and house price appreciation is not extensively studied in Australia. This research contributes to the literature on the effects of ethnic changes on house prices and implications of policy formulation to encourage multicultural communities.

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