The objective of this article is to identify important differences in the way new housing prices react to local and national economic factors. The study finds that regional housing prices react uniformly to certain national economic factors, such as mortgage rates. On the other hand, local factors such as population shifts, employment, and income trends often have a unique impact on housing prices. The study rejects the hypothesis of a single national housing market in favor of one that allows for broad national trends to be superimposed upon unique regional markets.The cyclical nature of the U.S. housing market has been well documen*ed in the academic literature. The focus of most of the previous work has ccntcred on either national housmg market macroeconomic relationships or microeconomic housing conditions for specific urban areas. The focus of this article is unique in that it analyzes the determinants of new housing prices at the regional level using a combination of macroeconomic and microstructural factors. Others have also recognized the need for a regional perspective. For example, McAvinchey and Maclennan (1982) postulate that housing inflation rates may differ across regions due to: (1) variations in preferences for housing relative to other goods, (2) significant differences and spatial rigidity in the structure of demand and supply which may inhibit equilibrating flows of households and construction inputs, and (3) national economic conditions which may not diffuse equally or impact all regions simultaneously (p. 44). Thus, in addition to identifying the specific factors that influence housing prices, this study analyzes the geographic structure of housing markets across the United States.Comparisons are made among nine census regions using a reduced form housing price function of the type employed by Manchester (1987) and Singell and Lillydahl (1990). The study finds that while various regions respond in a similar fashion to certain national factors, such as mortgage rates; local economic and demographic factors, such as population, employment, and income, have unique effects across different regions. In terms of geographic market structure, the results suggest that policy makers and housing researchers should consider at least four broad geographic regions (Northeast, North Central, South, and West) when formulating housing policy.
Economic conditions have placed increased importance upon rigorous financial analysis. In order to determine which analytical techniques are currently emp loyed by management, a questionnaire was sent to each fm on the May 1980, FORTUNE 500 list. The researchsought to establish a profile of the respondents' organizational structure and to identify the primary procedures used in risk assessment, working capital management, capital budgeting, and operations research modeling. The results do suggest a basic profile of the more active employers of analytical techniques. Relatively sophisticated capital budgeting procedures appear to be accepted across most industries, and many firms support their decision making with a “package” of formal tools.
Empirical studies examining the relationship between financial sector development and economic growth without including non-bank financial institutions (NBFIs) will likely generate biased empirical results. This study provides evidence that NBFIs can have a statistically significant negative impact on economic growth using cross-country data for both emerging and advanced countries. This finding suggests that these non-bank institutions, often loosely regulated, may introduce an excessive level of risk into the financial sector and the general economy. It is consistent with the current global financial crises where NBFIs, such as investment banks and insurance companies, introduced an excessive level of risk into the global economy. Hence, policy-makers may need to consider more timely and effective regulation of NBFIs and insure that adequate transparency and disclosure is provided to all financial markets participants.
Multiple discriminant analysis (MDA) is frequently used to develop statistical credit-scoring models for loan evaluation purposes. Current legislative efforts to insure that credit is being granted in a nondiscriminatory manner have focused considerable attention on the reliability of such models. This article examines the theoretical requirements of the MDA model in the context of a realistic lending situation and illustrates the extent of bias when these theoretical assumptions are not fully met. The article concludes that failure to rigorously meet all the theoretical assumptions of the statistical model may not be as critical as insuring that credit managers fully understand the limitations of these types of decision tools. Furthermore, the evidence indicates that statistical models other than multiple discriminant analysis are possibly more relevant to the credit-granting decision.
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