Search Results (6)

This study delineates transaction price submarkets of dwellings in Vienna by performing spatiotemporal clustering and analysing the change in purchasing prices in these clusters between 2018 and 2022. The submarkets are created using a novel spatiotemporal clustering method referred to as Multidimensional Spatiotemporal Change–DBSCAN (MDSTC-DBSCAN), which incorporates the temporal change in transaction prices along with spatial proximity to identify spatial areas with similar transaction prices. It represents an advancement over MDST-DBSCAN for this use case, as it considers the change over time as valuable information rather than a constraint that further splits the clustering groups. The results of the case study in Vienna indicate variations in price growth rates among the submarkets (i.e., contiguous regions with similar prices and price growth rates) that confirm the importance of considering the temporal changes in transaction prices. With respect to the Viennese case study, a lower Moran’s I value was observed for 2022 compared to previous years (2018 to 2021), indicating a higher level of homogeneity in transaction prices. This finding was also supported by the cluster analysis, as less expensive clusters demonstrated higher rates of price increase compared to more expensive clusters. Future research can enhance the algorithm’s usability and broaden its potential use cases to other multidimensional spatiotemporal event data. Full article

With the increasing availability of large volumes of space–time house data, delineating space–time housing submarkets is of interest to real estate agents, homebuyers, urban policymakers, and spatial researchers, among others. Appropriately delineated housing submarkets can help nurture submarket monitoring and housing policy developments. Although submarkets are often expected to represent areas with similar houses, neighborhoods, and amenities characteristics, delineating spatially contiguous areas with virtually no fragmented small areas remains challenging. Furthermore, housing submarkets can potentially change over time along with concomitant urban transformations, such as urban sprawl, gentrification, and infrastructure improvements, even in large metropolitan areas, which can complicate delineating submarkets with data for lengthy time periods. This study proposes a new method for integrating a random effects model with spatially constrained data-driven approaches in order to identify stable and reliable space–time housing submarkets, instead of their dynamic changes. This random effects model specification is expected to capture time-invariant spatial patterns, which can help identify stable submarkets over time. It highlights two spatially constrained data-driven approaches, ClustGeo and REDCAP, which perform equally well and produce similar space–time housing submarket structures. This proposed method is utilized for a case study of Franklin County, Ohio, using 19 years of space–time private house transaction data (2001–2019). A comparative analysis using a hedonic model demonstrates that the resulting submarkets generated by the proposed method perform better than popular alternative submarket creators in terms of model performances and house price predictions. Enhanced space–time housing delineation can furnish a way to better understand the sophisticated housing market structures, and to help enhance their modeling and housing policy. This paper contributes to the literature on space–time housing submarket delineations with enhanced approaches to effectively generate spatially constrained housing submarkets using data-driven methods. Full article

Over the past decade, urban density has been growing faster than ever, forcing high-density expansion. The aim of this study is to verify whether urban density is accepted as a sustainable value-added quality, internalized in the willingness to pay on a buildable per square feet basis. To explore the relationship between land prices and densification processes, this study focused on a low-density area, which recently went through a densification policy process with the approval of a new zoning tool. The study analyzes land price trends on a 144-Acre of area, located in the South Boston Submarket, identified as the Dorchester Ave Area. I analyzed land transactions in this area between 2012 and 2021. I also examined land price variations before and after the approval of a densification plan in correlation with the overall trend of the real estate market in that area. The results suggest that density is a value-added feature that affects land prices. Indeed, a higher density leads to higher values per buildable square feet. Densification policies have a strong positive impact on land transaction prices. Community and developers valued density with a greater willingness to pay, internalizing the economic, social, and environmental sustainability benefits. This phenomenon should be taken into consideration by local public authorities implementing their zoning tools. Full article

The accurate estimation of real estate value helps the development of real estate policies that can respond to the complexities and instability of the real estate market. Previously, statistical methods were used to estimate real estate value, but machine learning methods have gained popularity because their predictions are more accurate. In contrast to existing studies that use various machine learning methods to estimate the transactions or list prices of real estate properties without separating the building and land prices, this study estimates land price using a large amount of land-use information obtained from various land- and building-related datasets. The random forest and XGBoost methods were used to estimate 52,900 land prices in Seoul, South Korea, from January 2017 to December 2020. The models were also separately trained for different land uses and different time periods. Overall, the results revealed that XGBoost yields a higher prediction accuracy. Whereas the XGBoost models were more accurate on the 2020 data than on the 2017–2020 data when analyzing residential areas, the random forest models were more accurate on the 2017–2020 data than on the 2020 data. Further analysis will extend the prediction model to consider submarkets determined by price volatility and locality. Full article

This study analyzed the spillovers among intra-urban housing submarkets in Beijing, China. Intra-urban spillover imposes a methodological challenge for housing studies from the spatial and temporal perspectives. Unlike the inter-urban spillover, the range of every submarket is not naturally defined; therefore, it is impossible to evaluate the intra-urban spillover by standard time-series models. Instead, we formulated the spillover effect as a Markov chain procedure. The constrained clustering technique was applied to identify the submarkets as the hidden states of Markov chain and estimate the transition matrix. Using a day-by-day transaction dataset of second-hand apartments in Beijing during 2011–2017, we detected 16 submarkets/regions and the spillover effect among these regions. The highest transition probability appeared in the overlapped region of urban core and Tongzhou district. This observation reflects the impact of urban planning proposal initiated since early 2012. In addition to the policy consequences, we analyzed a variety of spillover “types” through regression analysis. The latter showed that the “ripple” form of spillover is not dominant at the intra-urban level. Other types, such as the spillover due to the existence of price depressed regions, play major roles. This observation reveals the complexity of intra-urban spillover dynamics and its distinct driving-force compared to the inter-urban spillover. Full article

In this paper, we investigate the influence of airport operation on property prices. In this research, we apply spatial hedonic regression and a difference-in-differences approach to address the introduction of new land use restrictions on property prices. We use data on housing transactions from two housing submarkets around regional airports in Poland. The results suggest that the introduction of land use restrictions impacts property prices. In general, as expected, more rigid restrictions translate into higher discounts in property prices. This research contributes to the limited knowledge on the impact of the introduction of land use restrictions on property prices, as most previous papers have focused solely on the impact of noise. These findings must be treated with caution, as some estimates were not statistically significant, mainly due to limited sample size. The research has important policy implications. Growing airports in Poland face tensions between economic and environmental sustainability. Currently, airports in Poland are obliged to limit their environmental impact by creating limited use areas related to the aircraft related noise while being responsible for property value loss related to these restrictions. As a consequence, most regional airports face significant compensations to property owners. Full article