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

Expert Recommended Biomedical Journal Articles: Their Retractions or Corrections, and Post-retraction Citing

Faculty Opinions has provided recommendations of important biomedical publications by domain experts (FMs) since 2001. The purpose of this study is two-fold: 1) identify the characteristics of the expert-recommended articles that were subsequently retracted; 2) investigate what happened after retraction. We examined a set of 232 recommended, later retracted or corrected articles. These articles were classified as New Finding (43%), Interesting Hypothesis (16%), etc. More than 71% of the articles acknowledged funding support; the NIH (US) was a top funder (64%). The top reasons for retractions were Errors of various types (28%); Falsification/fabrication of data, image, or results (20%); Unreliable data, image, or results (16%); and Results not reproducible (16%). Retractions took from less than two months to almost 14 years. Only 15 % of recommendations were withdrawn either after dissents were made by other FMs or after retractions. Most of the retracted articles continue to be cited post-retraction, especially those published in Nature, Science, and Cell. Significant positive correlations were observed between post-retraction citations and pre-retraction citations, between post-retraction citations and peak citations, and between post-retraction citations and the post-retraction citing span. A significant negative correlation was also observed between the post-retraction citing span and years taken to reach peak citations. Literature recommendation systems need to update the changing status of the recommended articles in a timely manner; invite the recommending experts to update their recommendations; and provide a personalized mechanism to alert users who have accessed the recommended articles on their subsequent retractions, concerns, or corrections

Statistical study of free magnetic energy and flare productivity of solar active regions

Photospheric vector magnetograms from Helioseismic and Magnetic Imager on board the Solar Dynamic Observatory are utilized as the boundary conditions to extrapolate both non-linear force-free and potential magnetic fields in solar corona. Based on the extrapolations, we are able to determine the free magnetic energy (FME) stored in active regions (ARs). Over 3000 vector magnetograms in 61 ARs were analyzed. We compare FME with ARs' flare index (FI) and find that there is a weak correlation ($<60\%$) between FME and FI. FME shows slightly improved flare predictability relative to total unsigned magnetic flux of ARs in the following two aspects: (1) the flare productivity predicted by FME is higher than that predicted by magnetic flux and (2) the correlation between FI and FME is higher than that between FI and magnetic flux. However, this improvement is not significant enough to make a substantial difference in time-accumulated FI, rather than individual flare, predictions.Comment: The paper was submitted to ApJ and it is accepted no