Variation In House Prices Research Articles

Predicting Housing Prices Using Supervised Machine Learning Models

Abstract: Variations in housing prices exert a profound impact on economic policies and personal financial decisions. This study aims to delve into the factors that affect housing prices and construct a predictive model using machine learning techniques. Machine learning enables computers to acquire knowledge from data and make predictions or decisions. This study forcasts house prices by analyzing the characteristics of data from Seattle, Washington using machine learning multiple linear regression, polynomial regression, and K-nearest neighbors regression (KNN). The findings of this investigation demonstrate that the polynomial regression model is the most accurate in predicting housing prices and can capture nonlinear relationships in the data more effectively than multiple linear regression and K-nearest neighbors regression. The main factors affecting housing prices are interior living space and the construction and design of buildings. These insights hold potential for enhancing government policies, facilitating effective land use decision-making by planners, and enabling investors to make more informed choices.

Urban Area Changes and Housing Price Variations in Chinese rapid urbanization regions

Urban Area Changes and Housing Price Variations in Chinese rapid urbanization regions

Radial analysis and scaling of housing prices in French urban areas

Urban scaling laws summarize how attributes evolve with city size. However, one limitation concerns the aggregate view of this approach, which leads to neglecting the internal structure of cities. This is an important issue regarding housing prices, given their significant variations across space. Based on a dataset compiling millions of real estate transactions over the period 2017–2021, we investigate the regularities of the radial (center-periphery) profiles of housing prices across cities, with respect to their size. Results are threefold. First, they corroborate prior findings in the urban scaling literature stating that largest cities agglomerate higher housing prices. Second, we find that housing price radial profiles scale in three dimensions with the power 1/5 of city population. After rescaling, great regularities between radial profiles can be observed, although some locational amenities have a significant impact on prices. Third, it appears that our approach with rescaled profiles fails to explain housing price variations in the city center across cities. In fact, prices near the city center rise much faster with city size than in the periphery. This has strong implications for low-income households seeking homeownership because prohibitive prices in the center may contribute to pushing them out into peripheral locations, especially in large cities.

Price Model for Transit-Oriented Developments in Kuala Lumpur, Malaysia

Abstract The idea of Transit Oriented Development (TOD) was to foster urban development around railway networks and has been strategically built and applied since the late 19th and early 20th centuries. Previous studies reported positive, negative, or irrelevant impact of TOD on prices of surrounding real estate. The study aims to evaluate the impact of TOD on property prices in Kuala Lumpur. It utilizes secondary data obtained from the National Property Information Centre (NAPIC), Malaysia, after data cleaning, Nine Thousand Five Hundred and Forty-Nine (9549) Housing Transactions between the periods 2009 and 2018 were used. The research design was quantitative, and the Hedonic Price Model (log-log model) was used for data analysis. The model revealed a multiple correlation coefficient (R) of 0.891 and an adjusted R2 value of 0.794, indicating that 79.4% of the house price variation is explained by the model. The F value of 996.921, which is statistically significant, indicates that the predictors significantly combine to predict the price of TOD areas in Kuala Lumpur. The coefficient for LnTrainsta is 0.056, indicating that there was a positive relationship between residential house prices and TOD in Kuala Lumpur; this explains that house prices increase by 5.6% for every 100 meter closer to the rail transit station.

Using The Regression Method to Discover the Relationship Between Real Estate and Stock Market

Recently, with the economic fluctuation, the relationship between house price and the stock market index has been paying much attention. However, this relationship has not been fully clarified yet through historical research. In this paper, several regression models including linear regression model and non-linear regression model are constructed to test for both single and dual relationship between the house price and market index. The assumption and method used in building the model will also be discussed. From testing, there is a positive linear effect on house price from the market index (approximately 80% of house price variation has been explained by the market index). On the opposite side, house price affects 93% of the market index in a positive non-linear pattern. Besides that, GDP can explain around 90% of house price variation in a positive linear relationship. This paper aims to help investors to have a better understanding of the relationship between these two and to help them build a better investment portfolio.

Housing markets and geographical labour mobility to high-productivity regions: The case of Stockholm

In many countries, there are signs of declining migration to high-productivity urban areas due to restrictions in the housing market and increasing regional differences in housing prices. Using detailed population-wide register data for Sweden, we estimate how regional variation in housing prices and homeownership is associated with the individual’s decision whether to accept a job offer in the Stockholm metropolitan region and the interrelated choice between migration and commuting as the mobility mode. Our findings indicate that high relative housing prices in the Stockholm area and homeownership are associated with decreasing total geographical labour mobility to the region. This is pronounced among the young and among highly skilled workers. The negative effects of high relative housing prices and homeownership on migration are partially but not fully compensated by positive effects on commuting to Stockholm.

Housing Market Dynamics in Kazakhstan: An Estimated DSGE Model

This study investigates the drivers of housing price fluctuations in Kazakhstan by using a dynamic stochastic general equilibrium (DSGE) model with the housing market. We estimate the model by using Bayesian methods with data for the period of 2010Q1 to 2020Q4. We find that housing preference shocks play a crucial role in explaining for housing market fluctuations. These shocks account for a substantial portion of housing price and consumption behavior variations. Surprisingly, monetary policy, government spending, productivity and markup shocks show limited explanatory power for housing price fluctuations. Our findings suggest the importance of monitoring potential housing bubbles, as well as the impact of housing market fluctuations on the broader economy, thus highlighting the housing wealth effect. Additionally, our analysis indicates that the pension withdrawal policy has a minor long-term effect on business-cycle fluctuations in Kazakhstan. Overall, technology shocks are key drivers of gross domestic product (GDP) variance, while inflation rate variation is mainly explained by monetary policy and foreign demand shocks.

Floating Population: Migration With(Out) Family and the Spatial Distribution of Economic Activity

This paper argues that migrants’ decision to bring their dependent family members shapes their consumption behavior, their choice of destination, and their sensitivity to migration barriers. We document that in China: (i) rural migrants disproportionately move to expensive cities; (ii) in these cities they live without their family and in poorer housing conditions; and (iii) they remit more, especially when living without their family. We then develop a quantitative general equilibrium spatial model in which migrant households choose whether, how (with or without their family), and where to migrate. We estimate the model using plausibly exogenous variation in wages, housing prices, and exposure to family migration costs. We use the model to estimate migration costs and relate them to migration policy. We find that hukou policies protect workers in large, expensive, and high income cities at the expense of rural households, who use remittances to overcome some of these costs.

The Role of Subjective Perceptions and Objective Measurements of the Urban Environment in Explaining House Prices in Greater London: A Multi-Scale Urban Morphology Analysis

House prices have long been closely related to the built environment of cities, yet whether the subjective perception (SP) of these environments has a differing effect on prices at multiple urban scales is unclear. This study sheds light on the impact of people’s SP of the urban environment on house prices in a multi-scale urban morphology analysis. We trained a machine learning (ML) model to predict people’s SP of the urban environment around properties across Greater London with survey response data from an online survey evaluating people’s SP of street view image (SVI) and linked this to house price data. This information was used to construct a hedonic price model (HPM) and to evaluate the association between SP and house price data in a series of linear regression models controlling location information and urban morphological characteristics such as street network centralities at multiple urban scales, quantified using space syntax (SS) methods. The findings show that SP influences house prices, but this influence differs depending on the urban scale of analysis. Particularly, a sense of ‘enclosure’ and ‘comfort’ are important factors influencing house price variation. This study contributes by introducing SP of the urban environment as a new dimension into the traditional HPM and by exploring the economic impact of SP on the house price market at multiple urban scales.

Erratum for ‘Delineating the Spatio‐Temporal Pattern of House Price Variation by Local Authority in England: 2009 to 2016’ by Chi et al. (2021)

Geographical AnalysisVolume 55, Issue 2 p. 342-342 ErratumFree Access Erratum for ‘Delineating the Spatio-Temporal Pattern of House Price Variation by Local Authority in England: 2009 to 2016’ by Chi et al. (2021) This article corrects the following: Delineating the Spatio-Temporal Pattern of House Price Variation by Local Authority in England: 2009 to 2016 Bin Chi, Adam Dennett, Thomas Oléron-Evans, Robin Morphet, Volume 54Issue 2Geographical Analysis pages: 219-238 First Published online: March 24, 2021 First published: 18 April 2023 https://doi.org/10.1111/gean.12329AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL In Chi et al. (2021), there was an error occurred in Abstract due to a production error. The word ‘80-year’ in the Abstract has been corrected to ‘8-year’, this error has been corrected in the article. Reference Chi, B., A. Dennett, T. Oléron-Evans, and R. Morphet. (2021). “Delineating the Spatio-Temporal Pattern of House Price Variation by Local Authority in England: 2009 to 2016.” Geographical Analysis 54(2), 219– 38. https://onlinelibrary.wiley.com/doi/10.1111/gean.12287. Volume55, Issue2April 2023Pages 342-342 ReferencesRelatedInformation

Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms

Conventional housing price prediction methods rarely consider the spatiotemporal non-stationary problem in a large data volumes. In this study, four machine learning (ML) models are used to explore the impacts of various features – i.e., property attributes and neighborhood quality - on housing price variations at different geographical scales. Using a 32-year (1984–2016) housing price dataset of Metropolitan Adelaide, Australia, this research relies on 428,000 sale transaction records and 38 explanatory variables. It is shown that non-linear tree-based models, such as Decision Tree, have perform better than linear models. In addition, ensemble machine learning techniques, such as Gradient-Boosting and Random Forest, are better at predicting future housing prices. A spatiotemporal lag (ST-lag) variable was added to improve the prediction accuracy of the models. The study demonstrates that ST-lag (or similar spatio-temporal indicator) can be a useful moderator of spatio-temporal effects in ML applications. This paper will serve as a catalyst for future research into the dynamics of the Australian property market, utilizing the benefits of cutting-edge technologies to develop models for business and property valuation at various geographical levels.

Collateral quality and house prices

This paper studies the effects of collateral quality shocks on house prices, the price-rent ratio, and the macroeconomy in a dynamic general equilibrium model with housing collateral. Collateral quality is not as perfect as is typically assumed in the existing literature on collateral constraints, and collateral quality shocks can simultaneously explain the salient features of the joint dynamics of house prices, the price-rent ratio, and output observed in the data. Moreover, depending on whether or not information about collateral quality is produced, there exist two lending regimes; endogenous switching between these two regimes also reinforces the patterns. I estimate this model using Bayesian methods and identify a conspicuous endogenous regime switch at the onset of the Great Recession. The results show that collateral quality shocks and the associated regime switch account for a large fraction of the variations in house prices and the price-rent ratio during the housing boom and bust of the 2000s.

House Pricing Prediction using ML Algorithm - A Comparative Analysis

The real estate industry is the least transparent in our environment. Housing prices fluctuate on a daily basis and are sometimes inflated rather than based on valuation. Since the housing industry is rapidly expanding, forecasting house prices is critical not just for businesses but also for individuals. However, there are other factors that influence house price variations. The purpose of this article is to forecast a real estate property's market value. The goal here is to build a prediction model for evaluating pricing based on characteristics that influence price. We are going to predict data using Machine learning algorithm i.e. linear regression model to predict the house pricing.

House prices, housing wealth and financial well-being

The causal chain between house price variation and observable changes in economic behavior including consumption requires that individuals perceive the impact of house price movements on their household wealth and recognize the impact on their financial situation. In this paper we establish evidence for the first link in this causal chain by investigating the effect of house price variation on perceived financial well-being using longitudinal information on subjective financial satisfaction. Exploiting substantial variation in local house prices in Australia across time, we find that plausibly exogenous unanticipated increases in local house prices improve the perceived financial well-being of home owners. The impact is strongest for mortgage owners, suggesting a strong collateral effect. We also highlight important asymmetries in the effects of house price shocks on perceived financial well-being associated with both the direction of the house price shock and tenure status. Mortgage owners exhibit more significant changes in their financial well-being in response to positive house price shocks. Furthermore, we highlight a weaker wealth effect for individuals that are liquidity constrained suggesting that these constraints are important for understanding responses to housing wealth shocks.

The interactions between Hong Kong’s housing market and global markets upon unprecedented monetary policy changes

This paper explores whether the introduction of unconventional monetary policy measures re-shapes the interactions between Hong Kong’s housing market and global stock market and economic factors. It is found that, the interactions between Hong Kong’s housing prices and global stock markets attenuated during the conventional monetary policy period (CMPP), especially after the Asian Financial Crisis. The level of interactions with global factors reached the lowest during the transitional period between CMPP and the unconventional monetary policy period (UMPP). By contrast, the co-movements with global stock market factors had played a bigger role in explaining Hong Kong’s housing price variations during UMPP, especially at the time when the US, UK, EU, and Japan deployed QEs and/or negative interest rate policy. Nevertheless, such high level of integrations did not sustain as QE3 ended in the US. The findings yield important implications in view of recent central bank actions responding to the dim economic prospects due to the COVID-19 pandemic.

Associations between Street-View Perceptions and Housing Prices: Subjective vs. Objective Measures Using Computer Vision and Machine Learning Techniques

This study investigated the extent to which subjectively and objectively measured street-level perceptions complement or conflict with each other in explaining property value. Street-scene perceptions can be subjectively assessed from self-reported survey questions, or objectively quantified from land use data or pixel ratios of physical features extracted from street-view imagery. Prior studies mainly relied on objective indicators to describe perceptions and found that a better street environment is associated with a price premium. While very few studies have addressed the impact of subjectively-assessed perceptions. We hypothesized that human perceptions have a subtle relationship to physical features that cannot be comprehensively captured with objective indicators. Subjective measures could be more effective to describe human perceptions, thus might explain more housing price variations. To test the hypothesis, we both subjectively and objectively measured six pairwise eye-level perceptions (i.e., Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity). We then investigated their coherence and divergence for each perception respectively. Moreover, we revealed their similar or opposite effects in explaining house prices in Shanghai using the hedonic price model (HPM). Our intention was not to make causal statements. Instead, we set to address the coherent and conflicting effects of the two measures in explaining people’s behaviors and preferences. Our method is high-throughput by extending classical urban design measurement protocols with current artificial intelligence (AI) frameworks for urban-scene understanding. First, we found the percentage increases in housing prices attributable to street-view perceptions were significant for both subjective and objective measures. While subjective scores explained more variance over objective scores. Second, the two measures exhibited opposite signs in explaining house prices for Greenness and Imageability perceptions. Our results indicated that objective measures which simply extract or recombine individual streetscape pixels cannot fully capture human perceptions. For perceptual qualities that were not familiar to the average person (e.g., Imageability), a subjective framework exhibits better performance. Conversely, for perceptions whose connotation are self-evident (e.g., Greenness), objective measures could outperform the subjective counterparts. This study demonstrates a more holistic understanding for street-scene perceptions and their relations to property values. It also sheds light on future studies where the coherence and divergence of the two measures could be further stressed.

Consumption and housing net worth: Cross-country evidence

Using a 22-country OECD panel from 1996 to 2017, we examine the extent to which the housing net worth channel affects personal consumption internationally during a period of considerable variation in house prices and credit provision. We identify an asymmetrical response where declines in housing net worth have a much greater impact on consumption than arises for positive changes in net worth.

CONSTRUCTING HOUSING PRICE INDEX FOR TERRACED PROPERTIES IN JOHOR BAHRU, MALAYSIA

Housing is a country’s biggest asset. Hence, the pattern of the housing price index (HPI) is an important topic to gain insight into the housing market while identifying the prevailing housing issues. The determinants of housing price vary for each city and state based on the different characteristics in each location. Accordingly, HPI should consider the property’s quality differences. Besides, national HPI is insufficient and restricted to the housing price at the state level. Thus, the study focused on constructing a specified HPI model for different cities, districts, and states. Effective HPI can give parties a better idea of the current property market situation and act as an analytical tool in managing the sector. Specifically, the study aims to examine the relationship between the heterogeneity housing attributes and housing prices of the terraced properties in Johor Bahru, Malaysia. Additionally, the study provides detailed information on the key determinants of the housing price variation in Johor Bahru. Hedonic price analysis is useful in constructing HPI, expressing housing price as a function of vector property characteristics. Furthermore, HPI is constructed based on the yearly indices and by pooling the data into certain periods. The results show the percentage of variance explained by the factors of HPI for the terraced properties in Johor Bahru. Correspondingly, the underlying correlation between the tested housing attributes with the housing price is explained through the analysis results.

Exploring a multilevel approach with spatial effects to model housing price in San José, Costa Rica

A multilevel model of the housing market for San José Metropolitan Region (Costa Rica) was developed, including spatial effects. The model is used to explore two main questions: the extent to which contextual (of the surroundings) and compositional (of the property itself) effects explain variation of housing prices and how does the relation between price and key covariates change with the introduction of multilevel effects. Hierarchical relations (lower level units nested into higher level) were modeled by specifying multilevel models with random intercepts and a conditional autoregressive term to include spatial effects from neighboring units at the higher level (districts). The random intercepts and conditional autoregressive models presented the best fit to the data. Variation at the higher level accounted for 16% of variance in the random intercepts model and 28% in the conditional autoregressive model. The sign and magnitude of regression coefficients proved remarkably stable across model specifications. Travel time to the city center, which presented a non-linear relation to price, was found to be the most important determinant. Multilevel and conditional autoregressive models constituted important improvements in modeling housing price, despite most of the variation still occurring at the lower level, by improving the overall model fit. They were capable of representing the regional structure and of reducing sampling bias in the data. However, the conditional autoregressive specification only represented a limited advance over the random intercepts formulation.

Housing bubbles and land planning corruption: evidence from Spain’s largest municipalities

Purpose The purpose of this paper is to quantify to what extent the housing bubble in the early-to-mid 2000s in Spain exacerbated land planning corruption among Spain’s largest municipalities. Design/methodology/approach The authors exploit plausibly exogenous variation in housing prices induced by changes in local mortgage market conditions; namely, the rapid expansion of savings banks (Cajas de Ahorros). Accounting for electoral competition in the 2003–2007 and 2007–2009 electoral cycles among Spanish municipalities larger than 25,000 inhabitants, the authors estimate a positive relationship between housing prices and land planning corruption in municipalities with variation in savings bank establishments using instrumental variables techniques. Findings A 1% increase in housing prices leads to a 3.9% points increase in the probability of land planning corruption. Moreover, absolute majority governments (not needing other parties’ support) are more susceptible to the incidence of corruption than non-majority ones. Two policy implications to address corruption emerge: enhance electoral competition and increase scrutiny over land planning decisions in sparsely populated. Originality/value First empirical evidence of a formal link between the 2000s housing bubble in Spain and land planning corruption.