An Improved Approach for Constructing the Real Estate Price Index with Bargaining Effect

Purpose A tangible representation of the housing market hotness, namely, the index derived via Carrilo 2013’s concept, is widely used but difficult to construct, as it requires listing data of a geography which is not only inaccessible but also cumbersome to summarize. The purpose of this paper is to serve as a methodological extension of Carrilo 2013, presenting a process by which the seller’s bargaining power index can be easily constructed using open-source data. Design/methodology/approach The seller’s bargaining power index was first constructed using the open-source data by Zillow Research. Then, results verification approach including visualization, Pearson correlation test, Granger causality test and linear regression were used with the goal to generate empirical evidence and verify if the constructed index conforms with established relationships derived in past work, thereby substantiate accuracy of the constructed index. Findings Monthly seller’s bargaining power index was constructed for US as a whole and 250 metropolitan statistical areas through an automated process. The constructed index conforms with established evidence in terms of seasonality, trend and magnitude. The index also forms positive correlation with building permit/housing value/housing value increase rate, and negative correlation with inventory. It requires the explanatory power of multiple linear features to simulate the parameter. Seller’s bargaining power index at a national level can serve as a general indicator of economic strength, in parallel to number of building permit, housing starts and real gross domestic product. Quantitatively, the housing market of the USA is as “hot” during the COVID-19 pandemic as it was in the years before 2008. A healthy housing market appears to be one in which 75%−80% of the deals are in favor of the seller (take-it-or-leave-it). When said percentage reaches 90%, the market should be considered to be in a “bubble.” Originality/value This paper provides an easy pathway for future studies to simulate housing market hotness, and bring upon the benefit of convenience at low cost and in monthly frequency rather than quarterly or annually.

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A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

Liu

et al. 2021

Entropy

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Background: the credit scoring model is an effective tool for banks and other financial institutions to distinguish potential default borrowers. The credit scoring model represented by machine learning methods such as deep learning performs well in terms of the accuracy of default discrimination, but the model itself also has many shortcomings such as many hyperparameters and large dependence on big data. There is still a lot of room to improve its interpretability and robustness. Methods: the deep forest or multi-Grained Cascade Forest (gcForest) is a decision tree depth model based on the random forest algorithm. Using multidimensional scanning and cascading processing, gcForest can effectively identify and process high-dimensional feature information. At the same time, gcForest has fewer hyperparameters and has strong robustness. So, this paper constructs a two-stage hybrid default discrimination model based on multiple feature selection methods and gcForest algorithm, and at the same time, it optimizes the parameters for the lowest type II error as the first principle, and the highest AUC and accuracy as the second and third principles. GcForest can not only reflect the advantages of traditional statistical models in terms of interpretability and robustness but also take into account the advantages of deep learning models in terms of accuracy. Results: the validity of the hybrid default discrimination model is verified by three real open credit data sets of Australian, Japanese, and German in the UCI database. Conclusion: the performance of the gcForest is better than the current popular single classifiers such as ANN, and the common ensemble classifiers such as LightGBM, and CNNs in type II error, AUC, and accuracy. Besides, in comparison with other similar research results, the robustness and effectiveness of this model are further verified.

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An Improved Approach for Constructing the Real Estate Price Index with Bargaining Effect

Cited by 3 publications

References 26 publications

Higher-order moment risk connectedness and optimal investment strategies between international oil and commodity futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict

Higher-order moment risk connectedness and optimal investment strategies between international oil and commodity futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict

Monthly seller’s bargaining power index constructed using open-source data

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

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