Impact of light rail line on residential property values – a case of Sydney, Australia

Purpose- 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. Design/methodology/approach- 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. Findings- 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. Practical implications- 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. Originality/value- 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

Using neural network model to estimate the rental price of residential properties

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Practical implications: 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. Originality/value: 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

Property valuation methods in practice: evidence from Australia

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Practical implications: 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. Originality/value: 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