An Innovative GIS-Based Territorial Information Tool for the Evaluation of Corporate Properties: An Application to the Italian Context

Locurcio, Marco; Morano, Pierluigi; Tajani, Francesco; Liddo, Felicia Di

doi:10.3390/su12145836

Cited by 27 publications

(19 citation statements)

References 120 publications

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“…The recent advancement of spatial datasets and tools, such as the geographic information system (GIS), has allowed for the implementation of GIS-based spatial analytic methods in housing price studies. One of the prominent applications of spatial analytics in housing price studies is the geographically weighted regression (GWR), which is a nonparametric weighted local regression technique that recognizes the spatial correlation of the observations [51]. When integrated with spatiotemporal data, the method is advantageous in characterizing the spatial heterogeneity and nonstationarity of housing prices, concerning potential price determinants at various spatial scales [52,53].…”

Section: Application Of Advanced Valuation Methodsmentioning

confidence: 99%

“…It is based on the evolutionary computation algorithms that aim to search for the polynomial structure and a set of explanatory vectors that best represents a system through continuous iteration. The methodology is advantageous in that it is capable of returning an explicit model expression for the relationship without requiring a pre-defined functional form and variables [51,56].…”

Section: Application Of Advanced Valuation Methodsmentioning

confidence: 99%

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Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)

2020

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Providing adequate public rental housing (PRH) of a decent quality at a desirable location is a major challenge in many cities. Often, a prominent opponent of PRH development is its host community, driven by a belief that PRH depreciates nearby property values. While this is a persistent issue in many cities around the world, this study proposed a new approach to assessing the impact of PRH on nearby property value. This study utilized a machine learning technique called long short-term memory (LSTM) to construct a set of housing price prediction models based on 547,740 apartment transaction records from the city of Busan, South Korea. A set of apartment characteristics and proximity measures to PRH were included in the modeling process. Four geographic boundaries were analyzed: The entire region of Busan, all neighborhoods of PRH, the neighborhoods of PRH in the “favorable,” and the “less favorable” local housing market. The study produced accurate and reliable price predictions, which indicated that the proximity to PRH has a meaningful impact on nearby housing prices both at the city and the neighborhood level. The approach taken by the study can facilitate improved decision making for future PRH policies and programs.

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Section: Application Of Advanced Valuation Methodsmentioning

confidence: 99%

Section: Application Of Advanced Valuation Methodsmentioning

confidence: 99%

Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)

2020

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“…Furthermore, the Structural Equation Models are explored as an alternative to the regression models for exploring the presence of latent variables [21][22][23]. Besides, other researches focus on the use of Neural Networks and Genetic Algorithms [24][25][26].…”

Section: Analysis Of Pricing In Housing Economicsmentioning

confidence: 99%

EPC Labels and Building Features: Spatial Implications over Housing Prices

2021

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The influence of building or dwelling energy performance on the real estate market dynamics and pricing processes is deeply explored, due to the fact that energy efficiency improvement is one of the fundamental reasons for retrofitting the existing housing stock. Nevertheless, the joint effect produced by the building energy performance and the architectural, typological, and physical-technical attributes seems poorly studied. Thus, the aim of this work is to investigate the influence of both energy performance and diverse features on property prices, by performing spatial analyses on a sample of housing properties listed on Turin’s real estate market and on different sub-samples. In particular, Exploratory Spatial Data Analyses (ESDA) statistics, standard hedonic price models (Ordinary Least Squares—OLS) and Spatial Error Models (SEM) are firstly applied on the whole data sample, and then on three different sub-samples: two territorial clusters and a sub-sample representative of the most energy inefficient buildings constructed between 1946 and 1990. Results demonstrate that Energy Performance Certificate (EPC) labels are gaining power in influencing price variations, contrary to the empirical evidence that emerged in some previous studies. Furthermore, the presence of the spatial effects reveals that the impact of energy attributes changes in different sub-markets and thus has to be spatially analysed.

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“…The paper by Marco Locurcio, Pierluigi Morano, Francesco Tajani and Felicia Di Liddo entitled "An Innovative GIS-Based Territorial Information Tool for the Evaluation of Corporate Properties: An Application to the Italian Context" [10] proposes an Automated Valuation Model for the corporate market segment, in order to support the investors', the credit institutions' and the public entities' decision processes. The application of the model to the corporate real estate segment market of the cities of Rome and Milan (Italy) outlines the potentialities of this approach in property big data management.…”

Section: Econometric Modelsmentioning

confidence: 99%