International Journal of Strategic Property Management

Development drivers for urban regeneration and livability in worn-out neighborhoods

2025-04-14T18:31:34+03:00

Development driver projects are recognized as tools for revitalizing and regenerating worn-out urban fabrics. Recent studies emphasize the importance of development driver projects for regeneration across three scales: macro, medium, and micro. However, research has lacked an examination of the impact of indicators at all three scales. To fill this research gap, the present study investigates the effect of development driver indicators on the regeneration of the worn-out fabric of Semnan, one of Iran’s historic and significant cities. This research initially identifies 16 indicators across the three scales. Data were collected from 385 residents and analyzed using the Phi coefficient and structural equation modeling. The results of the Phi test indicated that development projects could act as “nuclei for urban transformation of the worn-out fabric of Semnan”. Furthermore, the structural equation modeling analysis revealed that development driver indicators at the macro scale, such as parks (0.71) and landscape design (0.66), have a significant impact on regeneration. This study emphasizes the importance of a comprehensive approach to regeneration and suggests that active stakeholder participation in various stages of regeneration is essential. The findings of this study can serve as guidance for policymakers and urban planners in Iran and other countries.

Evaluating embedding models for text classification in apartment management

2025-04-23T18:31:43+03:00

The recent proliferation of embedding models has enhanced the accessibility of textual data classification. However, the crucial challenge is evaluating and selecting the most effective embedding model for a specific domain from a vast number of options. In this study, we address this challenge by assessing the performance of embedding models based on their effectiveness in downstream tasks. We analyze consultation records maintained by an apartment management body in South Korea, and convert this textual data into numerical representations using various embedding models. The vectorized text is then categorized using a k-means clustering algorithm. The downstream task, specifically, the classification of consultation records, is evaluated using a quantitative metric (Silhouette score) and qualitative approaches (domain-specific knowledge and visual inspection). The qualitative approaches yield more reliable results than the quantitative approach. These findings are expected to be valuable for the various stakeholders in property management.

Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model

2025-04-23T18:31:43+03:00

House prices have always been a popular indicator for real estate market monitoring. This study explores the spatiotemporal patterns of house prices at the community level in San Francisco from January 2009 to April 2024. A functional spatiotemporal semiparametric mixed effects (FST-SM) model was proposed to analyze the Zillow Home Value Index (ZHVI), considering spatiotemporal variations. This response is associated with known influences and unknown latent random effects. The random-effects component was expanded using functional principal components. The conditional autoregressive (CAR) structure of the principal component scores was adopted to analyze nonparametric time trends and spatiotemporal correlations. The proposed model was compared with other time-series models in terms of spatiotemporal prediction. The results show that the prediction accuracy of the proposed model is higher than that of other regular models. In summary, a functional mixed effects model was proposed to describe spatiotemporal patterns and forecast house prices. This study can provide valuable references for decision-making by local governments, real estate suppliers, and house buyers.