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Sustainability represents innovative elements in determining the profitability of real estate investments, among other factors, including the green component in real estate. Evidence from the literature has pointed out that incorporating green features into residential buildings can reduce operational costs and increase the building’s value. Although green real estate is considered the future trend of choice, it is still being determined whether prospective buyers are willing to accept the extra cost of green residential investment. Therefore, this study aims to investigate the effect of housing attributes and green certification on residential real estate prices.

The impact of the housing attribute and green certification in the residential sectors was assessed using a transaction data set comprising approximately 861 residential units sold in Selangor, Malaysia, between 2014 and 2022. Linear and quantile regression were used in this study by using SPSS software for a robust result.

The findings indicate that the market price of residential properties in Malaysia is influenced by housing attributes, transaction types and Green Building Index certification. The empirical evidence from this study suggests that green certification significantly affects the sales price of residential properties in Malaysia. The findings of this research will help investors identify measurable factors that affect the transaction prices of green-certified residential real estate. These identifications will facilitate the development of strategic plans aimed at achieving sustainable rates of return in the sustainable residential real estate market.

Specifically, this research will contribute to achieving area 4 of the 11th Malaysia Plan, which pertains to pursuing green growth for sustainability and resilience. This will be achieved by enhancing awareness among investors and homebuyers regarding the importance of green residential buildings in contributing to the environment, the economy and society.

The regression model for housing attributes and green certification on house price developed in this study could offer valuable benefits to support and advance Malaysia in realising its medium and long-term goals for green technology.

This paper aims to investigate the effects of school quality (SQ) on residential property prices in Johannesburg, South Africa. Previous studies have empirically examined the quality of private and public schools without a standard proxy that is accepted in the literature. As a result, this paper extends the literature to the global south by the effect that SQ has on residential property price changes in the local markets of the City of Johannesburg.

The research adopts the hedonic pricing model to evaluate and quantify the impact that the structural attributes such as erf size; number of bedrooms and bathrooms; and SQ measured by pass rates, sport rankings and quality of facilities have on house prices. A total of 2,763 property transactions covering the Kensington and Observatory areas of the City of Johannesburg over the period 2010 and 2020 were obtained from the deeds registry and used for the empirical analysis.

The study finds that SQ has a positive impact on house prices. When the average pass rate of the model school increases by 1%, all other things being equal, house prices also increase by 1.8%. This suggests that people who live closer to the model school are willing to pay more when the school performance improves. The 1.8% premium this study attributes to a 1% increase in school performance is however generally low when compared to some findings in the literature suggesting that there may be some other important factors that households consider when purchasing their home.

The main contribution is uncovering the relationship between the SQ and residential property prices in the local markets, using Kensington and Observatory in Johannesburg as sampled areas. Due to the presence of reliable and quality of data sets, such studies are not many in the global south and a study of this nature in South Africa is notably not existing in the literature.

The intent of this Practice Briefing is to provide clarity on drivers of property pricing in a changing economic environment. The principal basis of this analysis is to investigate how properties have been priced relative to interest rates over the long haul. Such an insight may help investors navigate the world of property investment in a post zero interest-rate policy (ZIRP) world.

This practice briefing is an overview of the role of economic drivers in pricing property in different economic eras pre- and post-ZIRP. It looks at returns over time relative to risk criteria and growth.

This briefing is a review of property pricing and its relationship to economic drivers and discusses the concept of return premiums as a market indicator to spot under/over-priced property assets in the market.

This briefing considers the implications of identifying salient and pertinent market indicators over time as bellweathers for property pricing. Good property investment is grounded in understanding when assets are under and overpriced relative to investors’ expectations of growth and returns going forward. An understanding of markets and the current indicators thereof can provide investors with insights into those criteria.

This provides guidance on how to interpret markets and get an understanding of property pricing over time.

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 research study aims to delve into the enduring relationship between housing property prices and economic policy uncertainty across eight major Indian cities.

Using the panel non-linear autoregressive distributed lag model, this study meticulously investigates the asymmetric impact of economic policy uncertainty on apartment and house (unit) prices in India during the period from 2000 to 2022.

The findings of this study indicate that economic policy uncertainty exerts a negative influence on property prices, but noteworthy asymmetry is observed, with positive changes in effect having a more pronounced impact than negative changes. This asymmetrical effect is particularly prominent in the case of unit prices.

This research reveals that long-run price trends are also influenced by factors such as interest rates, building costs and housing loans. Through a comprehensive analysis of these factors and their interplay with property prices, this research paper contributes valuable insights to the understanding of the real estate market dynamics in Indian cities.

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

This study aims to examine whether or not residential properties closer to landfill sites have lower offer values by the developers. That is, by analyzing real estate data and landfill site locations, the study seeks to provide insights into whether properties situated closer to landfill sites tend to have a lower offer values than those located farther away.

The study is exploratory in nature, and a case study approach is applied. A landfill site named “Uruli Devachi” is selected in the region of Pune district, and data is collected from 102 developers selling residential projects within a radius of 15 km (about 9.32 mi). The gathered data is analyzed by using basic descriptive statistics, one-way ANOVA and ordinary least squares (OLS) regression. The OLS regression helps to determine whether there is a relationship between the distance of a residential property from a landfill site and its offer value.

The findings suggest that landfill sites have a detrimental impact on residential property offer values, with the negative impact increasing with proximity to a landfill site. The negative effect seems to vanish after over 10 km (about 6.21 mi). The developers provide extra facilities including a clubhouse, a children’s play area, a gym and a swimming pool in an effort to mitigate the negative effects of the landfill site on residential properties.

The findings of this study could have implications for property developers, real estate professionals and policymakers in understanding how landfill proximity might impact property offer values.

This study presents many novelties for the Indian housing market: the landfill sites do have a negative effect on the offer value of residential property; the closer the residential property to a landfill site, the higher the negative effect. Further, the developers try and mitigate the negative effect of landfill sites on residential properties by providing additional amenities such as a clubhouse, children’s play park, gym and swimming pool.

By comparing regression models, this study aims to analyze the added home value of green sustainability features and green efficiency characteristics, rather than green certifications, in the city of Austin.

The adoption of home green energy efficiency upgrades has emerged as a new trend in the real estate industry, offering several benefits to builders and home buyers. These include tax reductions, health improvements and energy savings. Previous studies have shown that energy-certified single-family homes command a premium in the marketplace. However, the literature is limited in its analysis of the effects of green upgrades and certification on different types of single-family homes. To address this gap, this research collected data from 21,292 multiple listing services (MLS) closed home-selling listings in Austin, Texas, over a period of 35 months.

The analysis results showed that green efficiency features could generally increase single-family housing prices by 11.9%, whereas green sustainability upgrades can potentially bring a 11.7% higher selling price. Although green housing certification did not have significant effects on most housing groups, it did increase closing prices by 13.2% for single-family residences sold at the medium price range, which is higher than the impacts from simply listing the green features on MLS.

The study contributes to the body of knowledge by examining the market value of broadly defined energy efficiency and sustainability features in the residential housing market. The findings can help policymakers, brokerage firms, home builders and owners adjust their policies and strategies related to single-family home sales and mortgage approvals. The research also highlights the potential benefits of capitalizing on green housing features other than certifications.

This study aims to examine the effect of proximity and spatial dependence on the house price index for the nascent market Dar es Salaam, Tanzania. Despite the ongoing housing market transactions, there is no single house price index that takes into account proximity and spatial dependence. The proximity considerations in question are proximal to arterial roads, public hospitals, an airport and food markets. Previous studies on sub-Saharan Africa have focused on the ordinary least squares (OLS)-based hedonic model for the index and ignored spatial and proximity considerations.

Using the OLS and spatial econometric approach, the paper tests for the significance of the two effects – proximity and spatial dependence in the hedonic price model with year dummy variables from 2010 to 2019. The paper then compares the three indices in the following configurations: without the two effects, with proximity factors only, and with both effects, i.e. proximity and spatial dependence.

The inclusion of proximity factors and spatial dependence – spatial autocorrelation – seems to improve the hedonic price model but does not significantly improve the house price index. However, further research should be called for on account of the nascent nature of the market.

The paper brings new knowledge by demonstrating that it may not be necessary to take into account proximity factors and spatial dependence for the Dar es Salaam house price index.

The purpose of this paper is to evaluate the impact of beneficial externality generated by the World Heritage List (WHL) on residential property values in order to offer new insights into heritage discourses.

The study uses the hedonic price model to estimate empirically the difference in prices for residential properties located in the Old Rauma World Heritage. The study uses residential sales transaction data from the City of Rauma from January 2005 to September 2012 drawn from an online database called KVKL Hintaseurantapalvelu managed by the Central Federation of Finnish Real Estate Agencies.

The research results indicate a positive, but insignificant, relationship between the property sale prices (euros/sqm) and heritage designation. However, the total sale prices are higher in Old Rauma as the properties are significantly larger in Old Rauma compared to other properties in Rauma.

Studies in heritage economics have assessed the influence of the property market on heritage listing and designation at either the national level, the local level or a mix of national/local levels. This paper contributes to the literature by analysing the impact of a United Nations Educational, Scientific and Cultural Organisation (UNESCO) world heritage designation on residential property values. UNESCO is the leading global institution which deals with the protection of heritage sites that transcend national and local boundaries.