This paper investigates the value added by private information exchanges that share information on business payment performance. We discuss how this information is collected and disseminated by the world’s largest private information broker, Dun & Bradstreet. We provide the first empirical examination of the importance of this information at the lending decision level. Our findings indicate that exchange-generated information provides significant explanatory power in failure prediction models controlling for other credit information that is easily available to lenders. Our study complements the work of Jappelli and Pagano [Information sharing, lending and defaults; Cross country evidence, Centre for Economic Policy Research, Discussion Paper 2184, 1999] who find in cross-country macro level tests that information exchanges add value.

Jarl G. Kallberg, Crocker L. Liu, Charles Trzcinka, The Value Added from Investment Managers: An Examination of Funds of REITs, The Journal of Financial and Quantitative Analysis, Vol. 35, No. 3 (Sep., 2000), pp. 387-408

We examine the over-investment motivation for share repurchases using a sample of 139 Real Estate Investment Trusts (REITs) between 1996 and 2010. By combining a REIT's property portfolio data with project ROAs from the underlying real estate market, we are able to create a unique measure of the firm's investment opportunity set. Controlling for other possible buyback rationales, we find that poor investment opportunities are related to higher levels of share repurchases. Conditioning on investment opportunities, we find that the level of cash is positively related to repurchases only for low investment opportunity set firms. We also find a negative relationship between share repurchase announcement returns and investment opportunities.

This paper analyzes the optimal quality decision of a producer in a multi-period setting with reputation effects. Using a unique database of returns on real estate limited partnerships (RELPs), we empirically examine alternative theoretical predictions of optimal producer strategy. In particular, we test whether the producers in our market invest in reputation building by initially selling high quality goods and then lowering quality. Using a variety of statistical tests, we find evidence consistent with reputation building, both in the aggregate and for individual developers.