Automated Valuation Services : A case study for Aberdeen in Scotland

Acknowledgements: We are grateful to the three anonymous referees and the editor of the journal for suggestions and comments that helped to improve the paper. We thank seminar participants at the Technische Universit¨at Berlin, Fiona Stoddard, and Verity Watson for helpful comments. The usual disclaimer applies.Peer reviewedPostprin

AGRICULTURAL PRODUCERS' WILLINGNESS TO PAY FOR REAL-TIME MESOSCALE WEATHER INFORMATION

Mesoscale weather networks can provide improved weather information to agricultural producers. This technology can potentially improve production decisions, reduce irrigation and pesticide inputs, and reduce weather-related losses. Developing a mesoscale network to disseminate real-time mesoscale weather information requires a substantial investment. In addition, there are costs associated with maintenance of the system and distribution of the information available. While public funds may be available to support initial development of the system, there may be less public support initial development of the system, there may be less public support for maintaining the system and subsidizing usersÂ’' access to the information. This study uses the contingent valuation technique to determine the willingness of Oklahoma farmers and ranchers, as one set of potential users, to pay for real-time mesoscale weather information. The results indicate that agricultural producers are willing to pay only a modest fee for improved weather information. Gross sales, irrigation, and past weather losses are among the factors shown to significantly impact willingness to pay.Agribusiness,

Conformal prediction of real estate prices with machine learning

Mestrado Bolonha em Econometria Aplicada e PrevisãoA quantificação da incerteza associada à avaliação imobiliária tem sido notavelmente negligenciada na literatura. O objetivo deste trabalho é colmatar a lacuna existente, mediante uma análise da incerteza na avaliação de propriedades, através da aplicação de técnicas de aprendizado de máquina e previsão conformal. A previsão conformal quantifica a incerteza associada a previsões individuais e proporciona uma série de resultados possíveis em torno de estimativas pontuais com base em um nível de significância pré-definido. Ao aplicar a regressão de quantis conformal, somos capazes de mitigar as limitações das abordagens iniciais de regressão conformal e construir intervalos que exigem apenas que os dados sejam passíveis de intercâmbio para assegurar a cobertura. Através de um estudo empírico dos preços de imóveis na área da Baía de São Francisco, descobrimos que a regressão de quantis conformal fornece intervalos de previsão adaptativos com cobertura garantida que capturam variações inerentes à incerteza observada entre distintos níveis de preços de propriedades.Uncertainty quantification associated with real estate appraisal has largely been ignored in the literature. The aim of this dissertation is to fill this gap by analysing uncertainty in property valuation using machine learning complemented by conformal prediction. Conformal prediction quantifies uncertainty associated with individual predictions and provides a range of possible outcomes around point estimates based on a pre-defined significance level. By applying conformal quantile regression, we can mitigate limitations of early conformal regression approaches and we are able to build intervals that only require the data to be exchangeable for the coverage to be guaranteed. Through an empirical study of property prices in the San Francisco Bay Area, we find that the conformal quantile regression provides adaptive prediction intervals with guaranteed coverage that captures uncertainty variations across different property prices.info:eu-repo/semantics/publishedVersio