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The construction of new transportation infrastructure tends to affect the adjoining properties, economy and environment. In particular, studies have investigated the change in the value of properties due to increased access to transportation facilities. The purpose of this paper is to examine the impact of the recently completed light rail on residential property values in Sydney, Australia.

Sales data of residential properties was extracted from the CoreLogic’s RP database. The hedonic pricing model was used to assess the effect of proximity to the light rail stops. Two models were developed for the announcement and construction phases of the light rail project.

It was found that during the announcement phase, properties located within the 400 m radius from the station were 3.3% more expensive than those within the 400–800 radius. At the construction stage, the properties within the 0–400 m radius from the stops sold at 3.1% more than those within the 400–800 m radius. This study concludes that a positive relationship exists between the values of residential property and proximity to light rail stations.

These findings would be useful for policymakers to develop land value capture programs for infrastructure funding and to real estate professionals and investors for investment in future transit-oriented development.

Previous studies that aimed at examining the impact of light rails on residential properties values around universities are limited. Hence, this study provides a broad perspective on the impact of light rail on residential properties values.

Homeownership, especially for young adults, is a significant challenge in nearly every country and Indonesia, the fourth most populous country in the world, is not exempted. Its capital city, Jakarta, has the lowest homeownership rate when compared with other cities and if this challenge remains unresolved, it could lead to more social and economic issues in the country. Hence, this study aims to investigate the homeownership of young adults in Jakarta, focussing on young adults’ opinions, perceptions and experiences regarding homeownership opportunities.

A questionnaire survey was conducted to collect data from young adults in the study area. The collected data were analysed using the statistical package for the social sciences 24.0 software. Descriptive analysis, Cronbach’s alpha test, Pearson’s correlation test and mean score ranking were adopted to analyse the collected data.

The result shows that homeownership is driven by factors that are more functional and realistic (in terms of a place to live, marriage and parenthood) rather than those related to pride or social status representation (as a personal or career accomplishment). Unaffordability and insufficient income were ranked as crucial barriers to homeownership. Increasing the supply of affordable housing, controlling housing prices through government’s intervention and reducing mortgage interests are potential solutions to address this issue.

The result of this research would be useful to young adults who are the participants of this study, property developers, lending institutions and the government concerning homeownership policy formulation, loan provision, affordable housing supply, etc.

Specific studies that focussed on the young adults’ homeownership in Jakarta, Indonesia is limited, therefore, this research provides an insight into the issue of young adults’ homeownership in the country. Also, the findings could be applicable in other developing countries that have similar characteristics to Indonesia.

Booms and bubbles are inevitable in the real estate industry. Loss of profits, bankruptcy and economic slowdown are indicators of the adverse effects of fluctuations in property prices. Models providing a reliable forecast of property prices are vital for mitigating the effects of these variations. Hence, this study aims to investigate the use of artificial intelligence (AI) for the prediction of property price index (PPI).

Information on the variables that influence property prices was collected from reliable sources in Hong Kong. The data were fitted to an autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM) models. Subsequently, the developed models were used to generate out-of-sample predictions of property prices.

Based on the prediction evaluation metrics, it was revealed that the ANN model outperformed the SVM and ARIMA models. It was also found that interest rate, unemployment rate and household size are the three most significant variables that could influence the prices of properties in the study area.

The findings of this study provide useful information to stakeholders for policy formation and strategies for real estate investments and sustained growth of the property market.

The application of the SVM model in the prediction of PPI in the study area is lacking. This study evaluates its performance in relation to ANN and ARIMA.

Improving valuation accuracy, especially for sale and acquisition purposes, remains one of the key targets of the global real estate research agenda. Among other recommendations, it has been argued that the use of technology-based advanced valuation methods can help to narrow the gap between asset valuations and actual sale prices. The purpose of this paper is to investigate the property valuation methods being adopted by Australian valuers and the factors influencing their level of awareness and adoption of the methods.

An online questionnaire survey was conducted to elicit information from valuers practising in Australia. They were asked to indicate their level of awareness and adoption of the different property valuation methods. Their response was analysed using frequency distribution, χ² test and mean score ranking.

The results show that the traditional methods of valuation, namely, comparative, investment and residual, are the most adopted methods by the Australian valuers, while advanced valuation methods are seldom applied in practice. The results confirm that professional bodies, sector of practice and educational institutions are the three most important drivers of awareness and adoption of the advanced valuation methods.

There is a need for all the property valuation stakeholders to synergise and transform the property valuation practice in a bid to promote the awareness and adoption of advanced valuation methods, (e.g. hedonic pricing model, artificial neural network, expert system, fuzzy logic system, etc.) among valuers. These are all technology-based methods to improve the efficiency in the prediction process, and the valuer still needs to input reliable transaction data into the systems.

This study provides a fresh and most recent insight into the current property valuation methods adopted in practice by valuers practising in Australia. It identifies that the advanced valuation methods could supplement the traditional valuation methods to achieve good practice standard for improving the professional valuation practice in Australia so that the valuation profession can meet the industry’s expectations.

The demand for accurate property value estimation by valuation report end users has led to a shift towards advanced property valuation modelling techniques in some property markets and these require a sizeable number of data set to function. In a situation where there is a lack of a centralised transaction data bank, scholars and practitioners usually collect data from different sources for analysis, which could affect the accuracy of property valuation estimates. This study aims to establish the suitability of different data sources that are reliable for estimating accurate property values.

This study adopts the Lagos metropolis property market, Nigeria, as the study area. Transaction data of residential properties are collected from two sources, i.e. from real estate firms (selling price) and listing prices from an online real estate company. A portion of the collected data is fitted into the artificial neural network (ANN) model, which is used to predict the remaining property prices. The holdout sample data are predicted with the developed ANN models. Thereafter, the predicted prices and the actual prices are compared so as to establish which data set generates the most accurate property valuation estimates.

It is found that the listing data (listing prices) produced an encouraging mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) values compared with the firms’ data (selling prices). An MAPE value of 26.93 and 29.96 per cent was generated from the listing and firms’ data, respectively. A larger proportion of the predicted listing prices had property valuation error of margin that is within the industry acceptable standard of between ±0 and 10 per cent, compared with the predicted selling prices. Also, a higher valuation accuracy was recorded in properties with lower values, compared with expensive properties.

The opaqueness in real estate transactions consummated in developing nations could be attributed to why selling prices (data) could not produce more accurate valuation estimates in this study than listing prices. Despite the encouraging results produced using listing prices, there is still an urgent need to maintain a robust and quality property data bank in developing nations, as obtainable in most developed nations, so as to achieve a sustainable global property valuation practice.

This study does not investigate the relationship between listing prices and selling prices, which has been conducted in previous studies, but examines their suitability to improve property valuation accuracy in an emerging property market. The findings of this study would be useful in property markets where property transaction data bank is not available.

This study updates and extends the current work on the issue of accuracy of property valuation. The paper investigates the factors that contribute to property valuation inaccuracy and examines different strategies to achieve greater accuracy in practice.

An online questionnaire was designed and administered on the Australian Property Institute (API) registered valuers, attempting to examine their perceptions on the current state of valuation accuracy in Australia. The variables/statements from responses are ranked overall and compared for differences by the characteristics of respondents.

Using mean rating point, the survey ranked three factors; inexperience valuers, the selection, interpretation and use of comparable evidence in property valuation exercise and the complexity of the subject property in terms of design, age, material specification and state of repairs as the most significant factors currently affecting valuation inaccuracy. The results of a Chi-square test did not, however, show a significant statistical relationship between respondents' profile and the perception on the comparative importance of the factors identified. Except for valuers' age and inexperience valuers and valuers' educational qualification and inexperience valuers and the selection, interpretation and use of comparable evidence in property valuation exercise. Also, the three highly ranked strategies for reducing the level of inaccuracy are: developing a global mindset, use of advanced methodology and training valuers on market forecasting skills.

In order for valuers to provide state-of-the-art service to the public and to remain relevant, there is a need to accurately and reliably estimate valuation figures. Hence, the strategies highlighted in this study could be considered in a bid to reduce property valuation inaccuracy in practice.

This study provides an updated overview of the issue of property valuation inaccuracy in the Australia valuation practice and examines the strategies to reduce it.

Real estate property has been established as a composite good, and its value is determined by many variables. The heterogeneous nature of real estate property has made different stakeholders value these variables differently. Therefore, this study aims to identify and evaluate these sets of variables which influence residential property value in the Lagos metropolis property market, Nigeria, based on professional valuers’ perception.

A list of variables that influences property value was generated through literature review, and the list was used to design an online questionnaire that was administered to valuers practicing in the metropolis. The valuers were asked to rank these variables in order of significance. Their response was analysed to establish the mean score of each variable that depicts their level of significance.

In order of importance, property location, neighbourhood characteristics, property state of repair, size of property, availability of neighbourhood security and age of property are the most highly significant variables that are influential on the property value in the Lagos metropolis.

The findings of this study will inform all existing and prospective real estate stakeholders, including facility managers of the major determinants of the value of their investments and, at the same time, will be a tool for valuers and researchers in property value modelling.

This study is the first attempt to develop a framework of property value determinants in this research area in Nigeria.

The predictive accuracy and reliability of artificial intelligence models, such as the artificial neural network (ANN), has led to its application in property valuation studies. However, a large percentage of such previous studies have focused on the property markets in developed economies, and at the same time, effort has not been put into documenting its research trend in the real estate domain. The purpose of this paper is to critically review the studies that adopted ANN for property valuation in order to present an application guide for researchers and practitioners, and also establish the trend in this research area.

Relevant articles were retrieved from online databases and search engines and were systematically analyzed. First, the background, the construction and the strengths and weaknesses of the technique were highlighted. In addition, the trend in this research area was established in terms of the country of origin of the articles, the year of publication, the affiliations of the authors, the sample size of the data, the number of the variables used to develop the models, the training and testing ratio, the model architecture and the software used to develop the models.

The analysis of the retrieved articles shows that the first study that applied ANN in property valuation was published in 1991. Thereafter, the technique received more attention from 2000. While a quarter of the articles reviewed emanated from the USA, the rest were conducted in mostly developed countries. Most of the studies were conducted by universities scholars, while very few industry practitioners participated in the research works. Also, the predictive accuracy of the ANN technique was reported in most of the papers reviewed, but a few reported otherwise.

The articles that are not indexed in the search engines and databases searched and also not available in the public domain might not have been captured in this study.

The findings of this study reveal a gap between the valuation practice in developed and developing property markets and also the contributions of real estate practitioners and universities scholars to real estate research. A paradigm shift in the valuation practice in developing nations could lead to achieving a sustainable international valuation practice.

This paper presents the trend in this research area that could be useful to real estate researchers and practitioners in different property markets around the world. The findings of this study could also encourage collaboration between industry professionals and researchers domiciled in both developed and developing countries.

With the increased demand for the application of technology in property activities, there is a growing need for property professionals adept in using digital technology. Hence, it is important to assess the competence of academia in equipping property professionals with digital technology skills. This study, therefore, assesses property academics in Australian universities to identify their level of knowledge and use of digital technology applicable to the property industry.

Online questionnaire surveys were administered to 22 out of 110 property academics contacted through the Australia Property Institute (API) database to achieve this aim. The collected data were analysed using mean score ranking and ANOVA.

The study found that apart from databases and analytics platforms such as Corelogic RP data, price finder and industry-based software such as the Microsoft Office suite and ARGUS software, the academics were not knowledgeable in most identified and sampled proptech tools. Similarly, most proptech tools were not used or taught to the students. It was also found that early career academics (below five years in academia) were the most knowledgeable group about the proptech tools.

Relying on the API database to contact property academics potentially excludes the position of property academics who may not be affiliated or have contacts with API, hence, the findings of this study should be generalised with caution.

The study bears huge implications for the property education sector and industry in Australia; a low knowledge and use of nascent tools such as artificial intelligence, machine learning, blockchain, drones, fintech, which have received intense interest, reveals some level of skill gap of students who pass through that system and may need to be upskilled by employers to meet the current day demand.

In response to the clamour for technology-inclined property professionals, this paper presents itself as the first to assess the knowledge levels and application of digital technology by property academics.

Estimation of the rental price of a residential property is important to real estate investors, financial institutions, buyers and the government. These estimates provide information for assessing the economic viability and the tax accruable, respectively. The purpose of this study is to develop a neural network model for estimating the rental prices of residential properties in Cape Town, South Africa.

Data were collected on 14 property attributes and the rental prices were collected from relevant sources. The neural network algorithm was used for model estimation and validation. The data relating to 286 residential properties were collected in 2018.

The results show that the predictive accuracy of the developed neural network model is 78.95 per cent. Based on the sensitivity analysis of the model, it was revealed that balcony and floor area have the most significant impact on the rental price of residential properties. However, parking type and swimming pool had the least impact on rental price. Also, the availability of garden and proximity of police station had a low impact on rental price when compared to balcony.

In the light of these results, the developed neural network model could be used to estimate rental price for taxation. Also, the significant variables identified need to be included in the designs of new residential homes and this would ensure optimal returns to the investors.

A number of studies have shown that crime influences the value of residential properties. However, to the best of the authors’ knowledge, there is limited research investigating this relationship within the South African context.