International Journal of Strategic Property Management

Predictors of investment intention in real estate: Extending the theory of planned behavior

Akshita Singh, Shailendra Kumar, Utkarsh Goel, Amar Johri — Tue, 22 Oct 2024 00:00:00 +0300

This paper explores the factors affecting the investment intention of individual real estate investors utilizing the extended theory of planned behavior. With the help of self-administered questionnaire, data from 366 individual investors from India was collected. This data was analysed using two-step structural equation modelling. While significant direct effect of attitude, external influence, financial self-efficacy and perceived financial return was found, interpersonal influence, perceived financial risk, facilitating conditions and financial awareness had no significant direct impact on investment intention. Upon checking the mediating effect of attitude on the factors, all factors influenced investment intention through attitude except facilitating condition and financial awareness. It was also observed that attitude stands out as the most important aspect due to strongest influence on intention directly and also providing mediation to all variables except two. The study guides policymakers and investment institutions to develop strategies and utilize resources in a direction that can bring out a positive outcome by strengthening real estate investors’ investment intentions. It brings out the fact that financial confidence should be boosted by enabling investors to handle and manage their finances which can bring in a positive attitude for investing.

Human-made sounds in informal learning spaces on a university campus

Wed, 06 Nov 2024 00:00:00 +0200

The university property management department has been facing a challenge with the strategic management of campus spaces due to the need for informal learning spaces (ILSs) to facilitate students’ independent studies. However, there’s limited research on how these ILSs perform, particularly concerning human-made sounds. This study delves into the impact of human-made sounds on students, considering their individual differences and positive experiences in various types of ILSs within an Australian university campus. The investigation includes open-ended questions to delve deeper into students’ positive experiences with human-made sounds across different ILSs. The research findings demonstrate that the impact of human-made sounds on students is influenced by certain individual characteristics. Additionally, the study identifies three types of positive experiences regarding human-made sounds in ILSs for students: a sense of relaxation induced by human-made sounds, the sound-masking effect, and increased motivation for learning. This study could assist university property managers in understanding students’ perceptions of human-made sounds and aid in strategic management of campus space that aligns with students’ needs and preferences for ILSs, improving the overall learning environment and support student success.

Assessing the economic premium of additional elevator: PSM hedonic analysis in Beijing, China

Zhao Zhang, Yihua Mao, Yueyao Shui, Ruyu Deng — Wed, 20 Nov 2024 00:00:00 +0200

Although many Chinese cities have made remarkable progress in promoting age-friendly housings, few empirical studies related to the premium price of housings with additional elevators installed have been reported. In this study, we constructed propensity score matching–hedonic price models as well as quantile–hedonic models to assess the impact of adding elevators on housing prices. This study concludes: (1) The impact of additional elevators on housing prices varies depending on the floor level. (2) Single-facing and older housings are more likely to be negatively affected by the addition of elevators, and this interaction increases with floor level. (3) Adding an elevator reduces the price of low-priced housings on low floors and raises the price of high-priced housings on low floors, while for middle-floor and high-floor housings, the premium increases with the price of the housing. (4) Compared with the commercial community housings, the elevator premium of the affordable community housings is higher. Our findings provide an important reference for local governments to formulate appropriate charging and compensation mechanisms for adding elevators to multistory housings to promote retrofitting of aging communities.

Property appraisal via lens of property registration abundance – real estate market asymmetry assessment

Marek Walacik — Thu, 21 Nov 2024 00:00:00 +0200

Information on transaction prices and the ones characterizing the property as the subject of the valuation are essential for a proper valuation process. The accuracy and completeness of the collected set of information directly affects the quality of the valuation process. When market participants operate on the basis of unequal sets of information, information asymmetry is revealed. This research investigates the effects of information asymmetry on property market from the perspective of property registration abundance and mass appraisal systems. It explores how disparities in information abundance and quality within property registration and appraisal processes can affect market fairness and transparency. Employing a mixed-methods approach, it analyses property transaction data and tries to investigate effects of information asymmetry. The findings indicate that enhanced transparency and data quality can significantly reduce valuation discrepancies and lead to a more equitable real estate market. The study concludes with recommendations aimed at justifying information asymmetry’s negative effects, supporting for policies that promote information uniformity to improve the fairness and efficiency of property registration and mass appraisal practices.

Multidimensional house price prediction with SOTA RNNs

Yasin Kütük — Mon, 25 Nov 2024 00:00:00 +0200

This paper introduces insights into the Turkish real estate market, which can be generalized globally. It primarily aims to find the best forecasting algorithms for the housing price index and compare their prediction performance over three, six, nine, and twelve months ahead by using recurrent neural networks (RNN) with a comparison of out-of-sample predicting power of econometrical models. For these purposes, we employ three RNN architectures in twenty-four settings, revealing that certain RNN architectures are the best predictors in forecasting the Turkish real housing price index. The RNN architectures outperform traditional econometric models; however, the more months forecasted, the lower the prediction power. The lagged values of the price-to-rent ratio, real rents, and the lagged USDTRY values contribute more than the other predictors in forecasting the real housing price index. The outcomes suggest that stocks, real estate investment trusts, and gold are neither complementary nor competing financial instruments since housing is an illiquid asset.