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Land value varies at a micro level depending on the location’s economic, geographical and political determinants. The purpose of this study is to present a comprehensive assessment of the determinants affecting land value in the Indian city of Thiruvananthapuram in the state of Kerala.

The global influence of the identified 20 explanatory variables on land value is measured using the traditional hedonic price modeling approach. The localized spatial variations of the influencing parameters are examined using the non-parametric regression method, geographically weighted regression. This study used advertised land value prices collected from Web sources and screened through field surveys.

Global regression results indicate that access to transportation facilities, commercial establishments, crime sources, wetland classification and disaster history has the strongest influence on land value in the study area. Local regression results demonstrate that the factors influencing land value are not stationary in the study area. Most variables have a different influence in Kazhakootam and the residential areas than in the central business district region.

This study confirms findings from previous studies and provides additional evidence in the spatial dynamics of land value creation. It is to be noted that advanced modeling approaches used in the research have not received much attention in Indian property valuation studies. The outcomes of this study have important implications for the property value fixation of urban Kerala. The regional variation of land value within an urban agglomeration shows the need for a localized method for land value calculation.

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.

This study evaluated the effect of macroeconomic factors on residential real estate (RE) investment returns in the cities of Abuja and Lagos, Nigeria, with a view to guiding RE investors and researchers.

A survey research design was employed using a questionnaire to collect RE transaction data from 2008 to 2022 from estate surveying and valuation firms in the study areas. Rental and capital value data collected were used to construct rental and capital value indices and total returns on investment. The macroeconomic data used were retrieved from the archives of the Central Bank of Nigeria (CBN). Granger causality (GC) and multiple regression models were adopted to evaluate the effect of selected macroeconomic variables on residential RE investment returns in the study areas.

The study found a progressive upward movement in rental and capital values of residential RE investment in the study areas within the study period. Total and risk-adjusted returns on investment were equally positive within the study period. Only the inflation rate, unemployment rate and real gross domestic product (GDP) per capita were found to be the major determinants of residential RE investment returns in the study areas within the study period.

The secrecy associated with property transaction information/data by RE practitioners in the study areas posed a challenge. Property transaction data were not adequately kept in a way for easier access and retrieval in many of the estate firms and agent offices. Consequently, there was a lack of data that spanned the study period in some of the sampled estate firms or agent offices. This data collection challenge was, however, overcome by the excess time spent retrieving the required data for this study to ensure that the findings appropriately answer the research questions.

Inflation and GDP per capita have been found to be significant factors that influence residential RE investment performance in the study areas. Therefore, investors should pay attention to these identified macroeconomic factors for residential RE investment in the study areas whilst making investment decisions in order to mitigate a possible loss of income or return. The government should formulate and implement economic policies that would address the current high unemployment and inflation rates in Nigeria at large.

This study has extended and further enriched the existing body of knowledge in the field of RE investment analysis in Nigeria. To the best of the authors' knowledge, this study is the first to adopt the Cornish Fisher value-at-risk and modified Sharpe ratio models to analyse risk and risk-adjusted returns on residential RE investment, respectively, in Nigeria. It has therefore redirected the focus of RE researchers and practitioners to a more objective approach to RE investment performance analysis in Nigeria.

Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.

Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.

This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.

Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.

Unstructured data such as images have defied usage in property valuation for a long time. Instead, structured data in tabular format are commonly employed to estimate property prices. This study attempts to quantify the shape of land lots and uses the resultant output as an input variable for subsequent land valuation models.

Imagery data containing land lot shapes are fed into a convolutional neural network, and the shape of land lots is classified into two categories, regular and irregular-shaped. Then, the intermediate output (regularity score) is utilized in four downstream models to estimate land prices: random forest, gradient boosting, support vector machine and regression models.

Quantification of the land lot shapes and their exploitation in valuation led to an improvement in the predictive accuracy for all subsequent models.

The study findings are expected to promote the adoption of elusive price determinants such as the shape of a land lot, appearance of a house and the landscape of a neighborhood in property appraisal practices.

The purpose of this study is to examine the buyers’ preferences influencing the purchase of privately developed affordable housing in Kolkata and to determine whether unsold houses result from misalignment with these preferences.

The literature review and user-opinion survey identified 119 independent variables that indicate buyers’ preferences. A questionnaire survey of 383 households in affordable housing units from 32 housing complexes in Kolkata recorded buyers’ preferences and satisfaction against the independent variables grouped under five levels of characteristics. The product weights of variables derived from the rank sum method and percentage satisfaction give the Utility Score. Multivariate regression and univariate linear regressions were conducted to determine the significance of each Level of characteristics and each variable, identifying the significant variables that would affect the sale of affordable houses.

The multivariate regression analysis has indicated that 68.56% of the variation in the percentage of unsold houses was explained by the five utility scores, which affirms that misalignment with buyers’ preferences significantly affects the sale of privately developed affordable houses. Furthermore, building and neighbourhood-level utility show the highest significance as predictors, while city-level and miscellaneous utility have moderate significance, but housing complex-level utility lacks statistical significance.

This study addresses a research gap in privately developed affordable housing in Kolkata, enhancing understanding of buyer preferences in this segment.

The purpose of this paper is to examine the effect of visual technology on the price discovery process in listings of residential properties in Singapore from 2015 to 2018.

The authors empirically model the effects of 360 virtual tours and drone video on four dimensions in price discovery – buyers’ arrival rate, sale probability, transaction prices and time-on-market – using a comprehensive data set for the residential properties in Singapore.

The analysis shows that the availability of virtual tours or drone video in a listing increases the arrival rate from potential buyers, the probability of a successful sale and the selling price. These findings are consistent with the hypothesis that technologically enhanced tools improve the quality of information and the marketability of property. However, listings with virtual tours tend to be associated with longer marketing time, which is consistent with the prediction of the information overload hypothesis.

This paper extends the housing and price discovery literature by examining how technologically enabled new information affects property transactions.

To the best of the authors’ knowledge, this is the first paper to consider the impact of drone video on property market outcome.

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.

This study aims to explore the impact of oil prices on housing prices using Pakistani annual data from 1973 to 2021.

The Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests were used for unit-root testing, whereas the johansen-juselius test was used for cointegration. For the short-run, the error correction model is used and the robustness of the model is checked using the dynamic ordinary least squares (DOLS) and fully modified OLS (FMOLS). The cumulative sum (CUSUM) and CUSUM of Squares tests were used to check the stability of the model, while parameter instability was confirmed by the Chow breakpoint test. Finally, the impulse response function was used for causality.

According to the findings, rising oil prices, among other things, have an impact on housing prices. Inflation is the single most important factor affecting not only the housing sector but also the entire economy. Lending and exchange rates have a significant impact on housing prices as well. The FMOLS and DOLS results suggest that the OLS results are robust. According to the variance decomposition model, housing prices and oil prices are bidirectionally related. The Government of Pakistan must develop a housing policy on a regular basis to develop the country’s urban housing supply and demand.

It is suggested that in Pakistan, the rising oil prices is a problem for the housing prices as well as many other sectors. The government needs to explore alternative ways of energy generation rather than the heavy reliance on imported oil.

Pakistan has been experiencing rising oil prices and housing prices with the rapid urbanisation and rural–urban migration. The contribution to the literature is that neither attempt (as to the best of the authors’ knowledge) has been made to check the impact of rising oil prices on housing sector development in Pakistan.