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The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

Several studies have examined the impact of market fundamentals on house prices. However, the effect of economic sectors on housing prices is limited despite the existence of two-speed economies in some countries, such as South Africa. Therefore, this study aims to examine the impact of mining activities on house prices. This intends to understand the direction of house price spreads and their duration so policymakers can provide remediation to the housing market disturbance swiftly.

This study investigated the effect of mining activities on house prices in South Africa, using quarterly data from 2000Q1 to 2019Q1 and deploying an auto-regressive distributed lag model.

In the short run, we found that changes in mining activities, as measured by the contribution of this sector to gross domestic product, impact the housing price of mining towns directly after the first quarter and after the second quarter in the non-mining cities. Second, we found that inflationary pressure is instantaneous and impacts house prices in mining towns only in the short run but not in the long run, while increasing housing supply will help cushion house prices in both submarkets. This study extended the analysis by examining a possible spillover in house prices between mining and non-mining towns. This study found evidence of spillover in housing prices from mining towns to non-mining towns without any reciprocity. In the long run, a mortgage lending rate and housing supply are significant, while all the explanatory variables in the non-mining towns are insignificant.

These results reveal that enhanced mining activities will increase housing prices in mining towns after the first quarter, which is expected to spill over to non-mining towns in the next quarter. These findings will inform housing policymakers about stabilising the housing market in mining and non-mining towns. To the best of the authors’ knowledge, this study is the first to measure the contribution of mining to house price spillover.

The paper sets out a conceptualisation of the housing cycle centring on households' desire to upgrade their housing consumption.

The paper begins by studying house price trends and cycles in OECD countries since 2000 to identify housing cycle patterns. It then assesses existing theories partly in relation to these patterns. It then proposes a new conceptualisation of the housing cycle.

The paper finds the central role of supply lags in housing cycles is not warranted. Instead, a demand cycle generated by upgrading desires better explains an initial boom followed by a slow recovery.

The paper challenges existing orthodoxy on housing cycle dynamics and proposes an alternative perspective.

With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019.

The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework.

The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect.

Results here should be of use to policymakers in certain policy analysis.

The main purpose of this paper is to examine the convergence hypothesis of House Price Index (HPI) in the case of 18 major Indian cities for the period 2014–2019.

To attain the authors main goal, this study applies a clustering algorithm advanced by Phillips and Sul. This test creates a club of convergence based on the growth of the cities in terms of HPI.

The study findings show the existence of two convergence clubs and one non-convergent group. Club 1 includes the cities with high HPI growth, whereas club 2 comprises of cities with least HPI growth. Cities belonging to the non-convergent group are neither converging nor diverging.

This study findings will benefit home buyers, sellers, investors, regulators and policymakers interested in the dynamic interlinkages of house price (HP) among Indian cities.

The majority of the studies are conducted in the case of China at the province or city levels. Furthermore, in the case of India, none of the studies has investigated the HP club convergence across Indian cities. Therefore, the present study fills this research gap by examining the HP club convergence across Indian cities.

The purpose of this study is to carry out a comparative analysis of four machine learning models such as linear regression, decision trees, k-nearest neighbors and support vector regression in predicting housing prices in Prishtina.

Using Python, the models were assessed on a data set of 1,512 property transactions with mean squared error, coefficient of determination, mean absolute error and root mean squared error as metrics. The study also conducts variable importance test.

Upon preprocessing and standardization of the data, the models were trained and tested, with the decision tree model producing the best performance. The variable importance test found the distance from central business district and distance to the road leading to central business district as the most relevant drivers of housing prices across all models, with the exception of support vector machine model, which showed minimal importance for all variables.

To the best of the author’s knowledge, the originality of this research rests in its methodological approach and emphasis on Prishtina's real estate market, which has never been studied in this context, and its findings may be generalizable to comparable transitional economies with booming real estate sector like Kosovo.

This paper aims to examine the primary supply data sources that have been used for research into the sharing economy, and the advantages and limitations of these sources in the literature.

To address the research aims, this study conducted a systematic literature review and content analysis of all relevant articles. Following the review, the methodological sections of the selected papers were examined to identify the characteristics and limitations of all data sources used in the papers.

This study revealed several limitations of the use of three major data sources, namely, web scraping with self-made bots, inside Airbnb and AirDNA, for sharing economy research. The review shows that the majority of the selected papers did not acknowledge any limitations, nor did they discuss the quality of the data sources.

The findings of this paper can serve as guidelines for selecting appropriate data sources for research into the sharing economy and cautions researchers to address the limitations of the data sources used.

To the best of the authors’ knowledge, this is the first study that explores the advantages and limitations of data sources used in short-term rental market research.

This paper aims to explore the relationship between market pricing and design quality within the development industry. Currently, there is a lack of research that examines real estate at the property level. Development quality is widely believed to have diminished over the past decades, while many investors seem uninterested in the design process. The study aims to address these issues through a pricing model that integrates design attributes. It is hoped that empirical findings will invite broader stakeholder interest in the design process.

The research establishes a framework for assessing spatial compliance across residential developments within London. Compliance is assessed across ten boroughs, with technical space guidelines used as a proxy for design quality. Transaction prices and spatial assessments are aligned within a hedonic pricing model. Empirical findings are used to establish whether undermining spatial standards presents a significant development risk.

Findings suggest a relationship between sale time and unit size, with “compliant” units typically transacting earlier than “non-compliant” units. Almost half of the 1,600 apartments surveyed appear to undermine technical guidelines.

It is suggested that an array of design attributes be explored that extend beyond unit size. Additionally, future studies may consider the long-term implications of design quality via secondary transaction prices.

Practical implications include the development of a more scientific approach to design valuation. This may enhance the position of product design management within the development industry and architectural services.

Social implications may include improvement in residential design.

An innovative approach combines a thorough understanding of both design and economic principles.

The purpose of this study is to explore the intricate relationship between oil prices, house prices in the UK and Norway, and the mediating role of gold and stock prices in both the short- and long-term, unraveling these complex linkages by employing an empirical approach.

This study benefits from a comprehensive set of econometric tools, including a multiequation vector autoregressive (VAR) system, Granger causality test, impulse response function, variance decomposition and a single-equation autoregressive distributed lag (ARDL) system. This rigorous approach enables to identify both short- and long-run dynamics to unravel the intricate linkages between Brent oil prices, housing prices, gold prices and stock prices in the UK and Norway over the period from 2005:Q1 to 2022:Q2.

The findings indicate that rising oil prices negatively impact house prices, whereas the positive influence of stock market performance on housing is more pronounced. A two-way causal relationship exists between stock market indices and house prices, whereas a one-way causal relationship exists from crude oil prices to house prices in both countries. The VAR model reveals that past housing prices, stock market indices in each country and Brent oil prices are the primary determinants of current housing prices. The single-equation ARDL results for housing prices demonstrate the existence of a long-run cointegrating relationship between real estate and stock prices. The variance decomposition analysis indicates that oil prices have a more pronounced impact on housing prices compared with stock prices. The findings reveal that shocks in stock markets have a greater influence on housing market prices than those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

This study may have several limitations. First, the model does not include all relevant macroeconomic variables, such as interest rates, unemployment rates and gross domestic product growth. This omission may affect the accuracy of the model’s predictions and lead to inefficiencies in the real estate market. Second, this study does not consider alternative explanations for market inefficiencies, such as behavioral finance factors, information asymmetry or market microstructure effects. Third, the models have limitations in revealing how predictors react to positive and negative shocks. Therefore, the results of this study should be interpreted with caution.

These findings hold significant implications for formulating dynamic policies aimed at stabilizing the housing markets of these two oil-producing nations. The practical implications of this study extend to academics, investors and policymakers, particularly in light of the volatility characterizing both housing and commodity markets. The findings reveal that shocks in stock markets have a more profound impact on housing market prices compared with those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

These findings could also serve as valuable insights for future research endeavors aimed at constructing models that link real estate market dynamics to macroeconomic indicators.

Using a variety of econometric approaches, this paper presents an innovative empirical analysis of the intricate relationship between euro property prices, stock prices, gold prices and oil prices in the UK and Norway from 2005:Q1 to 2022:Q2. Expanding upon the existing literature on housing market price determinants, this study delves into the role of gold and oil prices, considering their impact on industrial production and overall economic growth. This paper provides valuable policy insights for effectively managing the impact of oil price shocks on the housing market.

This research developed a model to understand and predict housing market dynamics and evaluate the significance of house permits data in the model’s forecasting capability.

The research uses a multilevel algorithm consisting of a machine-learning regression model to predict the independent variables and another regressor to predict the dependent variable using the forecasted independent variables.

The research establishes a statistically significant relationship between housing permits and house prices. The novel approach discussed in this paper has significantly higher prediction capabilities than a traditional regression model in forecasting monthly average prices (R-squared value: 0.5993), house price index prices (R-squared value: 0.99) and house sales prices (R-squared value: 0.7839).

The impact of supply, demand and socioeconomic factors will differ in various regions. The forecasting capability and significance of the independent variables can vary, but the methodology can still be applicable when provided with the considered variables in the model.

The resulting model is helpful in the decision-making process for investments, house purchases and construction as the housing demand increases across various cities. The methodology can benefit multiple players, including the government, real estate investors, homebuyers and construction companies.

Existing algorithms and models do not consider the number of new house constructions, monthly sales and inventory in the real estate market, especially in the United States. This research aims to address these shortcomings using current socioeconomic indicators, permits, monthly real estate data and population information to predict house prices and inventory.