[ABE-L] Seminário DEST/UFMG em 21/06/2024

[Marcos Prates]

Caros,

Na próxima sexta-feira (21 de Junho, às 13:30h) o ciclo de Seminários do
Departamento de Estatística da UFMG terá a apresentação do Prof. James
Sweeney da University of Limerick - Ireland.

James Sweeney is an Associate Professor of Statistics at the Department of
Mathematics and Statistics at the University of Limerick, Ireland. He
received a PhD in Statistics from Trinity College Dublin, Ireland, in 2012,
working on the development of Bayesian spatio-temporal statistical models
for estimating past climate change from fossil pollen assemblages under
Professor John Haslett. His main research fields of interests are in
Bayesian design of experiments, with application in pharmaceutical clinical
trial design, and the development of spatio-temporal statistical models for
applications including: renewable energy forecasting (wind and solar
forecasting), property-price modelling, and development of models for
infectious disease spread.

Title: What is the Impact of Postcodes on Dublin House Prices?

 Accurate and efficient valuation of property is of utmost importance in a
variety of settings, including when securing mortgage finance to purchase a
property, or where residential property taxes are set as a percentage of a
property’s resale value. Internationally, resale based property taxes are
most common due to ease of implementation and the difficulty of
establishing site values. In an Irish context, property valuations are
currently based on comparison to recently sold neighbouring properties.
However, this approach is limited by low property turnover. National
property taxes based on property value, as opposed to site value, also act
as a disincentive to undertake improvement works due to the ensuing
increased tax burden. We have developed a spatial hedonic regression model
that separates the spatial and non-spatial contributions of property
features to resale value. We mitigate the issue of low property turnover
through geographic correlation, borrowing information across multiple
property types and finishes. We investigate the impact of address
mislabelling on predictive performance, where vendors erroneously have
given a more affluent postcode, and evaluate the contribution of
improvement works to increased values. Our flexible geo-spatial model
outperforms all competitors across a number of different evaluation
metrics, including the accuracy of both price prediction and associated
uncertainty intervals. While our models are applied in an Irish context,
the ability to accurately value properties in markets with low property
turnover and to quantify the value contributions of specific property
features has widespread application. The ability to separate spatial and
non-spatial contributions to a property’s value also provides an avenue to
site-value based property taxes.

O seminário será transmitido ao vivo pelo canal do Youtube "Seminários DEST
- UFMG".

https://www.youtube.com/@seminariosdest-ufmg

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