JournalTOCs

International Journal of Strategic Property Management
Journal Prestige (SJR): 0.344

Citation Impact (citeScore): 2
Number of Followers: 3

This is an Open Access Journal

Open Access journal
ISSN (Print) 1648-715X - ISSN (Online) 1648-9179
Published by Vilnius Gediminas Technical University

[15 journals]

Real estate valuation models performance in price prediction
- Authors: Adela Deaconu, Anuța Buiga, Helga Tothăzan
  Pages: 86 - 105
  Abstract: Using a sample of 900 apartments from Cluj-Napoca, Romania, containing selling transactions for the second semester of 2019, and data for 33 locational, physical and neighbourhood-related attributes (socio-cultural, environmental, and urbanism related), our research objective is to test the performance in price prediction, and hence the utility, of the Artificial Neural Networking (ANN), as artificial intelligence model versus the Generalized Linear Model (GLM), as a regression model. By contributing to an ongoing debate, our empirical findings confirm the results of a predominant group of earlier studies, namely the superiority of ANN. Precisely, we found that ANN can better predict selling prices and provides stability of results. Additionally, we addressed the critiques related to the transparency of results, showing that ANN also has the ability to illustrate the significance of the different attributes of real estate, if appropriate statistical indicators are used. These findings can serve the different real estate valuation purposes, including that of the review of valuation reports.
  PubDate: 2022-02-11
  DOI: 10.3846/ijspm.2022.15962
  Issue No: Vol. 26, No. 2 (2022)
Evaluation of infrastructure projects by a decision model based on RPR,
MABAC, and WASPAS methods with interval-valued intuitionistic fuzzy sets
- Authors: Sina Salimian, Seyed Meysam Mousavi, Jurgita Antuchevičienė
  Pages: 106 - 118
  Abstract: Infrastructure projects (IPs) face numerous challenges to reach the predefined aims over their life-cycle. There are many difficulties in projects because of the variety of elements in project’s tendency and the dependency of the project on mainly national factors. Due to these difficulties and their practices, the projects meet with uncertainty. In this paper, an interval-valued intuitionistic fuzzy set (IVIFS) is used at identifying ambiguity in IPs. Also, a new multi-criteria decision-making (MCDM) model is presented to evaluate and select the suitable alternative in IPs. Hence, a new IVIF-relative preference alternative-multi-attributive border approximation area comparison (IVIF-RPR-MABAC), and IVIF-weighted aggregated sum product assessment (IVIF-WASPAS) are proposed in order to obtain the weights of decision makers (DMs) and criteria, and a new IVIF-RPR-MABAC method is proposed to rank the alternatives. In this paper, a combination of the three mentioned approaches creates proposed new hybrid model to evaluate the main factors and the projects. Furthermore, a real case study is applied from the literature to validate the efficiency and performance of the proposed model. Afterward, a comparative analysis is presented to validate the proposed approach by comparing the hybrid proposed model with two IVIF-TOPSIS and IVIF-extended-VIKOR methods. The final results confirm the efficiency of the proposed model in ranking the main alternatives of an MCDM problem. Moreover, the sensitivity analysis is reported to determine the affection of parameters on the final weighting and ranking outcomes.
  PubDate: 2022-02-22
  DOI: 10.3846/ijspm.2022.16476
  Issue No: Vol. 26, No. 2 (2022)
Forecasting spatially correlated targets: simultaneous prediction of
housing market activity across multiple areas
- Authors: Changro Lee
  Pages: 119 - 126
  Abstract: This study involved the development of an approach to forecast house prices and trading volumes across multiple areas simultaneously. Spatially correlated targets, such as house prices, can be predicted more accurately by leveraging the correlations across adjacent areas. A multi-output recurrent neural network, a deep learning algorithm specifically developed to analyze sequence data, was utilized to forecast the house prices and trading volumes in the four chosen study areas. The forecasting accuracy of future house prices in one of the four geographical areas clearly improved; this area was found to be a price-lagging area, and the forecasting accuracy of this area significantly increased by exploiting the information of a price-leading area. As for the prediction of trading volumes, the difference in performance between the multi-output recurrent neural network and conventional models was very small. The results of this study are expected to promote the use of deep learning to predict the housing market activity through a simultaneous forecasting framework.
  PubDate: 2022-04-11
  DOI: 10.3846/ijspm.2022.16786
  Issue No: Vol. 26, No. 2 (2022)
Path analysis on escalation of commitment of investors in different
project scenarios of PPPs
- Authors: Qi Luo, Ruolan Gao, Jicai Liu, Yahui Li
  Pages: 127 - 140
  Abstract: In public-private partnerships (PPPs), escalation of commitment (EOC) of investors often occurs when receiving negative feedback, leading to a great waste of resources and not conducive to the sustainable development of PPPs. The degree of project completion and decision-making responsibilities of investors with different conscientiousness and neuroticism may affect subsequent resource allocation and further influence their escalation behaviour. Thus, through scenario simulation, this paper constructs path analysis to illustrate the formation mechanism of EOC by investors with diverse conscientiousness and neuroticism in different degrees of decision-making responsibilities and project completion, in which confidence in completing the projects may present a mediating effect. The empirical results show that completion degree and decision-making responsibilities both positively affect investors’ EOC and that the interaction is significant. The impact mechanism of conscientiousness and neuroticism on EOC varies in different project scenarios. Then, some targeted recommendations are proposed to curb EOC. The findings provide scientific evidence for governments to conduct effective governance of EOC in PPPs.
  PubDate: 2022-04-21
  DOI: 10.3846/ijspm.2022.16477
  Issue No: Vol. 26, No. 2 (2022)
The impacts of task complexity, overconfidence, confirmation bias,
- Authors: Chun-Chang Lee, Hsueh-Yung Lee, Wen-Chih Yeh, Zheng Yu
  Pages: 141 - 155
  Abstract: Real estate valuation relies on real estate appraisers’ accurate assessments, which reflects the need to improve the objectivity of the valuation process. From the perspective of behavioral economics, appraisers are prone to numerous behavioral conflicts that could result in variations in their valuations. This study investigates the impacts of task complexity, overconfidence, confirmation bias, client influence, and anchoring on variations in real estate valuations. An online questionnaire was administered to 272 members of the Taiwan Real Estate Appraisers Association LINE group. Structural equation modeling was employed for analysis. A total of 150 valid responses were collected, yielding a valid response rate of 55.15%. The empirical results revealed that cognitive bias and client influence have significant and positive impacts on anchoring. Task complexity, overconfidence, and customer influence have significant and positive impacts on valuation variation. In addition to being the first study to explore the relationship between task complexity and valuation variation, this study also explored unconventional issues, such as the relationships between overconfidence, confirmation bias, and valuation variation. The results highlight the salience and value of behavioral economics-based analyses in empirical research on real estate valuation.
  PubDate: 2022-04-27
  DOI: 10.3846/ijspm.2022.16704
  Issue No: Vol. 26, No. 2 (2022)
Property management enabled by artificial intelligence post Covid-19: an
exploratory review and future propositions
- Authors: Farheen Naz, Anil Kumar, Arvind Upadhyay, Hemakshi Chokshi, Vaidotas Trinkūnas, Robert Magda
  Pages: 156 - 171
  Abstract: The Covid-19 pandemic outbreak across the globe has disrupted human life and industry. The pandemic has affected every sector, with the real estate sector facing particular challenges. During the pandemic, property management became a crucial task and property managers were challenged to control risks and disruptions faced by their organizations. Recent innovative technologies, including artificial intelligence (AI), have supported many sectors through sudden disruptions; this study was performed to examine the role of AI in the real estate and property management (PM) sectors. For this purpose, a systematic literature review was conducted using structural topic modeling and bibliometric analysis. Using appropriate keywords, the researchers found 175 articles on AI and PM research from 1980 to 2021 in the SCOPUS database. A bibliometric analysis was performed to identify research trends. Structural topic modelling (STM) identified ten emerging thematic topics in AI and PM. A comprehensive framework is proposed, and future research directions discussed.
  PubDate: 2022-05-10
  DOI: 10.3846/ijspm.2022.16923
  Issue No: Vol. 26, No. 2 (2022)