https://journals.vilniustech.lt/index.php/IJSPM <p>The International Journal of Strategic Property Management publishes original interdisciplinary research on strategic management of property.&nbsp;<a href="https://journals.vilniustech.lt/index.php/IJSPM/about">More information ...</a></p> Vilnius Gediminas Technical University en-US International Journal of Strategic Property Management 1648-715X <p>Copyright © 2021 The Author(s). Published by Vilnius Gediminas Technical University.</p> <p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</p> https://journals.vilniustech.lt/index.php/IJSPM/article/view/21184 <p>The study aims to investigate the heterogeneity of the Yangtze River’s impact on housing prices, using the data of 12,325 residential transactions within 8 kilometers of the Yangtze River in Wuhan, based on submarkets divided according to geographical location and buyer groups. The kernel density plots reveal that properties near the Yangtze River have the highest price and the lowest density, while properties further away from the river exhibit the opposite trend. Then the Spatial Generalized Additive Model and the Spatial Quantile Generalized Additive Model show the following results, respectively: (1) The Yangtze River has an influence range of roughly 5 kilometers on adjacent dwellings, with an average impact of 0.035%. However, within the chosen geographical interval, the impact rises from 1.582% to 2.072%. (2) The Yangtze River has the greatest impact on middle-priced houses, followed by high-priced houses, and the least impact on low-priced houses. (3) The Spatial Generalized Additive Model and the Spatial Quantile Generalized Additive Model have been proven to be effective at capturing spatial and temporal impacts on data. In conclusion, this article advises that the government should pay more attention to non-central locations with limited natural resources.</p> Xiaoling Ke Chang Yang Moujun Zheng Mougharbel Amal Yanshan Zeng Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0 2024-04-04 2024-04-04 28 2 76–92 76–92 10.3846/ijspm.2024.21184 https://journals.vilniustech.lt/index.php/IJSPM/article/view/21214 <p>Although vacancies in the real estate market have always been of key interest to both private and public stakeholders, measuring the impact of vacancy rates on property prices is challenging. This study attempts to quantify the extent of the influence of vacancy rates on the price decline of commercial properties. The dataset used in this study was collected and collated by valuation experts. We attempt to alleviate the bias inherent in the price estimates provided by the surveyors. A Bayesian multilevel estimation model was employed, and the results revealed that while some experts do not show deviations from the standard tendency of a peer group, others do diverge from this norm. Based on these findings, a bias-controlling price decline rate was derived. As many real estate statistics are produced based on survey data compiled by experts, the approach adopted in this study is expected to be applied to various real estate practices to mitigate the inherent and inevitable surveyor bias.</p> Changro Lee Copyright (c) 2024 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0 2024-04-04 2024-04-04 28 2 93–100 93–100 10.3846/ijspm.2024.21214