TY - GEN
T1 - Spatial Automated Valuation Model (sAVM) – From the Notion of Space to the Design of an Evaluation Tool
AU - Marques, João Lourenço
AU - Batista, Paulo
AU - Castro, Eduardo Anselmo
AU - Bhattacharjee, Arnab
N1 - Funding Information:
The authors are grateful to the two reviewers for many helpful comments and suggestions which helped us improve upon the paper. The usual disclaimer applies. This work is an output of the research project DRIVIT-UP-DRIVIng forces of urban Transformation: assessing pUblic Policies, Grant/Award Number: POCI-01?0145-FEDER-031905; Research Unit on Governance, Competitiveness and Public Policy (GOVCOPP), Grant/Award Number: UID/CPO/04058/2019.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/9/11
Y1 - 2021/9/11
N2 - Assuming that it is not possible to detach a dwelling from its location, this article highlights the relevance of space in the context of housing market analysis and the challenge of capturing the key elements of spatial structure in an automated valuation model: location attributes, heterogeneity, dependence and scale. Thus, the aim is to present a spatial automated valuation model (sAVM) prototype, which uses spatial econometric models to determine the value of a residential property, based on identification of eight housing characteristics (seven are physical attributes of a dwelling, and one is its location; once this spatial data is known, dozens of new variables are automatically associated with the model, producing new and valuable information to estimate the price of a housing unit). This prototype was developed in a successful cooperation between an academic institution (University of Aveiro) and a business company (PrimeYield SA), resulting the Prime AVM & Analytics product/service. This collaboration has provided an opportunity to materialize some of fundamental knowledge and research produced in the field of spatial econometric models over the last 15 years into decision support tools.
AB - Assuming that it is not possible to detach a dwelling from its location, this article highlights the relevance of space in the context of housing market analysis and the challenge of capturing the key elements of spatial structure in an automated valuation model: location attributes, heterogeneity, dependence and scale. Thus, the aim is to present a spatial automated valuation model (sAVM) prototype, which uses spatial econometric models to determine the value of a residential property, based on identification of eight housing characteristics (seven are physical attributes of a dwelling, and one is its location; once this spatial data is known, dozens of new variables are automatically associated with the model, producing new and valuable information to estimate the price of a housing unit). This prototype was developed in a successful cooperation between an academic institution (University of Aveiro) and a business company (PrimeYield SA), resulting the Prime AVM & Analytics product/service. This collaboration has provided an opportunity to materialize some of fundamental knowledge and research produced in the field of spatial econometric models over the last 15 years into decision support tools.
KW - Housing market analysis
KW - Spatial automated valuation model
KW - Spatial econometric models
UR - http://www.scopus.com/inward/record.url?scp=85115995988&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-86973-1_6
DO - 10.1007/978-3-030-86973-1_6
M3 - Conference contribution
AN - SCOPUS:85115995988
SN - 9783030869724
T3 - Lecture Notes in Computer Science
SP - 75
EP - 90
BT - Computational Science and Its Applications. ICCSA 2021
PB - Springer
T2 - 21st International Conference on Computational Science and Its Applications 2021
Y2 - 13 September 2021 through 16 September 2021
ER -