How accurate are commercial-real-estate appraisals? evidence from 25 years of NCREIF sales data

In this study, we provide new evidence on the performance measurement and reporting of commercial real estate returns. We do so by examining the accuracy of commercial-real-estate appraisals that occurred prior to the sale of properties from the NCREIF National Property Index (“NPI”) during 1984 – 2010, a period which spans two up-and-down cycles of the market. We find that, on average, appraisals are more than 12% above, or below, subsequent sales prices that take place two quarters following the appraisal. Even in a portfolio context, allowing for offsetting positive and negative differences, appraisals are off by an average of 4% – 5 % of value, even after adjusting for capital appreciation during those two quarters. We also provide new evidence regarding how, and by how much, appraised values lag behind sales prices. We find that appraisals appear to lag the true sales prices, falling significantly below in hot markets and remaining significantly above in cold markets. This new evidence provides guidance to investors, regulators and others about how to interpret real-estate indices like the NPI that are based upon appraised values, in both a rising and falling market. Finally, we find that this “appraisal error” is largely systematic; we can explain more than half of the variation in the signed percentage difference in sales price and appraised value. Hence, appraisal errors are not due solely to property-specific heterogeneity.appraisal; commercial real estate; commingled real estate fund; NCREIF; real estate

The role of commercial real estate investments in the banking crisis of 1985-92

This article examines the role of commercial real estate investments in the banking crisis of 1985-92, an unprecedented period during which more than 1,300 banks failed. Bank failures are fundamentally important because of the unique role played by financial institutions in the provision of business credit. We discover three striking features of banks failing during this period. First, commercial real estate was only a factor in the bank failures of 1988-92. Second, construction loans played a much larger role in bank failures than permanent loans, and the relationship is strongest with construction loans booked during 1983-1985. Third, other ex ante risk measures are systematically related to banking failure throughout the sample period. These results suggest that risk-seeking banks brought about their own demise and commercial real estate, especially construction lending, was one of the vehicles.bank; bank failure; commercial bank; commercial real estate; construction lending; real estate

A CAMEL rating's shelf life

How quickly do the CAMEL ratings regulators assign to banks during on-site examinations become "stale"? One measure of the information content of CAMEL ratings is their ability to discriminate between banks that will fail and those that will survive. To assess the accuracy of CAMEL ratings in predicting failure, Rebel Cole and Jeffery Gunther use as a benchmark an offsite monitoring system based on publicly available accounting data. Their findings suggest that, if a bank has not been examined for more than two quarters, off-site monitoring systems usually provide a more accurate indication of survivability than its CAMEL rating. The lower predictive accuracy for CAMEL ratings “older” than two quarters causes the overall accuracy of CAMEL ratings to fall substantially below that of off-site monitoring systems. The higher predictive accuracy of off-site systems derives from both their timeliness—an updated off-site rating is available for every bank in every quarter—and the accuracy of the financial data on which they are based. Cole and Gunther conclude that off-site monitoring systems should continue to play a prominent role in the supervisory process, as a complement to on-site examinations.bank; bank failure; CAMEL; CAMEL rating; commercial bank; offsite supervision

Who needs credit and who gets credit? Evidence from the Surveys of Small Business Finances

In this study, we use data from the Federal Reserve’s 1993, 1998 and 2003 Surveys of Small Business Finances to classify small businesses into four groups based upon their credit needs and to model the credit allocation process into a sequence of three steps. First, do firms need credit? We classify those that do not as “non-borrowers;” these firms have received scant attention in the literature even though they account for more than half of all small firms. Second, do firms need credit but fail to apply because they feared being turned down? We classify such firms as “discouraged borrowers.” Like non-borrowers, discouraged borrowers have received little attention in the literature and often are pooled with firms who applied for, but were denied, credit. Discouraged borrowers outnumber firms that applied for, but were denied, credit by more than two to one. Third, do firms apply for credit, but get turned down? We classify such firms as “denied borrowers.” Finally, we classify firms that applied for, and were extended, credit as “approved borrowers.” Our results reveal strong and significant differences among each of these four groups of firms. Non-borrowers look very much like approved borrowers, consistent with the Pecking-Order Theory of capital structure. Discouraged borrowers resemble denied borrowers in many respects, but are significantly different along a number of dimensions. This finding calls into question the results from previous studies that have pooled together these two groups of firms in analyzing credit allocation. Finally, we find strong evidence that denied borrowers differ from approved borrowers across numerous characteristics, as previously documented in the literature. Of particular note, minority owned-firms, and especially Black-owned firms, were denied credit at a far higher rate than firms with owners who were white.availability of credit; capital structure; discrimination; entrepreneurship; small business; SSBF

Gender and the availability of credit to privately held firms: Evidence from the surveys of small business finances

This study analyzes differences by gender in the ownership of privately held U.S. firms and examines the role of gender in the availability of credit. Using data from the nationally representative Surveys of Small Business Finances, which span a period of sixteen years, we document a series of empirical regularities in male- and female-owned firms. Looking at the differences by gender, we find that female-owned firms are 1) significantly smaller, as measured by sales, assets, and employment; 2) younger, as measured by age of the firm; 3) more likely to be organized as proprietorships and less as corporations; 4) more likely to be in retail trade and business services and less likely to be in construction, secondary manufacturing, and wholesale trade; and 5) inclined to have fewer and shorter banking relationships. Moreover, female owners are significantly younger, less experienced, and not as well educated. We also find strong univariate evidence of differences in the availability of credit to male- and female-owned firms. More specifically, female-owned firms are significantly more likely to be credit-constrained because they are more likely to be discouraged from applying for credit, though not more likely to be denied credit when they do apply. However, these differences are rendered insignificant in a multivariate setting, where we control for other firm and owner characteristics

An ERTS-1 investigation for Lake Ontario and its basin

The author has identified the following significant results. Methods of manual, semi-automatic, and automatic (computer) data processing were evaluated, as were the requirements for spatial physiographic and limnological information. The coupling of specially processed ERTS data with simulation models of the watershed precipitation/runoff process provides potential for water resources management. Optimal and full use of the data requires a mix of data processing and analysis techniques, including single band editing, two band ratios, and multiband combinations. A combination of maximum likelihood ratio and near-IR/red band ratio processing was found to be particularly useful