Pricing Factors in Real Estate Markets: A Simple Preference Based Approach

Conventional wisdom tells us that the price level of properties should be supported by the rent they receive. This paper examines the pricing factors of properties by analyzing how individuals allocate their income to housing consumption and other goods, which in turn become the rent (or implicit rent) to support property values. Our model’s results can explain several puzzling observations in property markets, including why the variance of property appreciation rates is much higher than that of income growth rates in the same area.Preference-based model, pricing factors, property appreciation, property markets

A Rational Explanation for Boom-and-Bust Price Patterns in Real Estate Markets

This paper develops a stylized model to provide a rational explanation for the boom-and-bust price movement pattern that we frequently observe in the real world. Our stylized model indicates that there are three conditions to form a boom-and-bust price pattern in a community: a move-in of high income residents, wide income gap between new and existing residents, and supply process that leads to an inventory buildup. It seems that, based on these three conditions, China is more likely to experience a boom-and-bust price movement pattern than a developed country with a more mature and less vibrant economy.Real Estate Cycles; Boom-and-Bust; Supply Decision; Moving Costs

Antimicrobial peptide identification using multi-scale convolutional network

Background: Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results: In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions: Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN

Financing knowledge, risk attitude and P2P borrowing in China

The advance of Internet technology has provided a convenient market platform for matching lending and borrowing parties, but many consumers still hesitate to use online borrowing. To better understand consumer behaviour in online borrowing, we use nationally representative survey data in China to explore factors affecting consumer use of one type of online borrowing, person to person (P2P) borrowing. Through empirical analyses, we find that financing knowledge and risk attitude are two key factors associated with P2P borrowing. Results show that financing knowledge is directly associated with P2P borrowing, while risk attitude through an instrument variable is associated with P2P borrowing also. Given these results, an effective way to expand the consumer Internet borrowing market in China is to enhance consumer financial literacy education

Home Equity-Based Borrowing and Corporate Financing : Evidence from Norwegian Private Firms

Home equity-based borrowing has been blamed in the literature for causing financial instability (e.g., Mian and Sufi, 2011). In this study, we examine the bright side of home equity-based borrowing. Using Norwegian administrative data, we investigate the relationship between financial constraints and business activities through the collateral channel. The finding suggests that owners’ home equity withdrawals are positively associated with new equity injections into their closely held existing private firms. When an owner’s home experiences substantial house price appreciation and when the owner has lower leverage, this relationship becomes more pronounced. We observe significant and enduring operational improvements within firms, along with higher survival rates in the post-extraction period. We also find that home equity extraction motivates experienced owners to replicate their success by establishing new companies in the same industry and contributes to the promotion of regional entrepreneurial activities. Our findings provide insightful evidence for understanding how owners of small and medium-sized enterprises (SMEs) finance their firms. The findings in our paper generate important policy implications in that support a need of credit relaxation and assessment on ultimate impacts of lending policies