Impact of sports arenas on land values: evidence from Berlin

This paper develops a hedonic price model explaining standard land values in Berlin. The model assesses the impact of three multifunctional sports arenas situated in Berlin-Prenzlauer Berg which were designed to improve the attractiveness of their formerly deprived neighbourhoods. Empirical results confirm expectations about the impact of various attributes on land values. Sports arenas have significant positive impacts within a radius of about 3,000 m. The patterns of impact vary, indicating that the effective impact depends on how planning authorities address potential countervailing negative externalities

Evaluating avms performance. beyond the accuracy

Automated Valuation Models (AVMs) are regularly used in mass appraisal techniques. Thanks to developments in artificial intelligence, machine learning algorithms are increasingly being used alongside traditional econometric models. The final phase in the definition of the models consists in the verification phase of the results elaborated by Avm. The predictive effectiveness tests evaluate the models trained on part of the dataset (the training set) and then measure their ability to predict the remaining values of the dataset (testing set). This verification methodology provides as final output the accuracy parameter, i.e. the difference between predicted prices and actual prices. According to many authors this parameter, if considered alone, is insufficient. The research consists in an accuracy test of 5 Avm in the ability to predict the values of 1038 properties in the city of Padua. To the accuracy results of the test are added the results of cross-validation and the use of different statistical indicators for the measurement of predictive effectiveness. The results provide useful information that broadens the framework of model knowledge. They can be used in the analysis and description of automated evaluation models

Homogeneous feature transfer and heterogeneous location fine-tuning for cross-city property appraisal framework

Most existing real estate appraisal methods focus on building accuracy and reliable models from a given dataset but pay little attention to the extensibility of their trained model. As different cities usually contain a different set of location features (district names, apartment names), most existing mass appraisal methods have to train a new model from scratch for different cities or regions. As a result, these approaches require massive data collection for each city and the total training time for a multi-city property appraisal system will be extremely long. Besides, some small cities may not have enough data for training a robust appraisal model. To overcome these limitations, we develop a novel Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning (HFT+HLF) cross-city property appraisal framework. By transferring partial neural network learning from a source city and fine-tuning on the small amount of location information of a target city, our semi-supervised model can achieve similar or even superior performance compared to a fully supervised Artificial neural network (ANN) method.Comment: Accepted by AusDM 201

The Cross Validation in Automated Valuation Models: A Proposal for Use

The appraisal of large amounts of properties is often entrusted to Automated Valuation Models (AVM). At one time, only econometric models were used for this purpose. More recently, also machine learning models are used in mass appraisal techniques. The literature has devoted much attention to assessing the performance capabilities of these models. Verification tests first train a model on a training set, then measure the prediction error of the model on a set of data not met before: the testing set. The prediction error is measured with an accuracy indicator. However, verification on the testing set alone may be insufficient to describe the model\u2019s performance. In addition, it may not detect the existence of model bias such as overfitting. This research proposes the use of cross validation to provide a more complete and effective evaluation of models. Ten-fold cross validation is used within 5 models (linear regression, regression tree, random forest, nearest neighbors, multilayer perception) in the assessment of 1,400 properties in the city of Turin. The results obtained during validation provide additional information for the evaluation of the models. This information cannot be provided by the accuracy measurement when considered alone