Why public transit can be good for business, even in the auto-oriented Sunbelt

Rail transport is expensive for cities to build and maintain, but many cities have gotten around this by building light rail systems in recent decades. In new research, Kevin Credit examines how businesses are affected by new light rail transit systems. He finds that areas within one mile of stations have nearly 30 percent more retail businesses, 40 percent more ..

Alien Registration- Credit, Emilie (Biddeford, York County)

https://digitalmaine.com/alien_docs/1775/thumbnail.jp

Development Credit Corporation of Maine: Annual Report 1959

Some ten years ago certain bankers and business men of Maine conceived the idea of some organization which would help to fill the gap which existed in our financial structure and assist in part, at least, those concerns which were unable to satisfy their financial requirements through ordinary bank credit or the capital market. As a result of their deliberations, study and planning, the Development Credit Corporation of Maine was formed, financed largely by member banking institutions, backed up by capital advanced by Maine businesses. This corporation was formed along the lines of the free enterprise system with no state or governmental guaranties of any kind, with bankers and businessmen assuming the full risks of loss and with no special tax advantages. Today we have ten years of experience behind us. The Corporation has made seventy- four loans totaling $2,186,282.78. The amount of loans paid out in full has amounted to$978,727.95, and partial payments were $236,982.80, with losses of$48,015.10. At the time the Credit Corporation was formed, there was considerable skepticism in the minds of many who feared that losses resulting from our loans of such a risk nature would soon dissipate its available funds. This skepticism has proved to be unfounded. Losses over the ten-year period have amounted to 2.2% of total loans made, or two-tenths of one percent per year. Considering the risk nature of our loans, we feel that this is quite a satisfactory ratio. In fact, if it had been less, we would not have been doing the job which we had undertaken. Includes photographs of buildings assisted by this program, including Sylvania Electric Products in Waldoboro, Maine; Hillcrest Poultry Company in Lewiston, Maine; Edwards Company in Pittsfield, Maine; Maine Paper Tube Corporation in South Gardiner, Maine; Viner Bros. Inc. in Bangor, Maine; Commonwealth Shoe & Leather Company in Gardiner, Maine; Bonnar-Vawter in Rockland, Maine; and Bangor Shoe Manufacturing Company in Bangor, Maine.https://digicom.bpl.lib.me.us/books_pubs/1337/thumbnail.jp

The Pandemic Economy

Since its emergence in 2019, the worldwide spread of the novel coronavirus SARS-CoV-2 (COVID-19) has created a vast economic crisis as government lockdowns place considerable strain on businesses of all kinds – particularly those that rely on face-to-face contact, such as retail restaurants, and personal services. Given the recent emergence of the virus and lags in data collection and publication, the highest-quality fine-grained spatial datasets on economic behavior will not reflect virus-related impacts for at least a year. At the same time, in order to make evidence-based decisions on policies regarding continuing lockdown and/or re-opening policies, local governments and researchers need to understand neighborhood-level economic effects much sooner than that. This paper makes use of the point-level Chicago Business License dataset, which is updated on a weekly basis, to examine the impact of the COVID-19 pandemic on new business activity in the City of Chicago. The results indicate that on average, from March to September 2020, total monthly new business starts have declined by 33.4% compared to the monthly average of new starts in the City from January 2016 to December 2019. Food service and retail businesses have been hardest hit during this period, while chains of all types have seen larger average declines in new startup activity than independent businesses. These patterns demonstrate interesting intra-urban spatial heterogeneity; ZIP codes with the largest pandemic-related declines in new business activity tend to be have larger rates average rates of new business creation to begin with and also have less dense, diverse, and walkable built environments (defined in more detail below), while, interestingly, observed COVID-19 case rates do not appear to have an individually-significant impact on new business deficits

Credit Suisse 2011 Third Quarter Earnings Report

Credit Suisse Asset Management Income Fund, Inc. (Reports Third Quarter Earnings

Perspectives on credit scoring and fair mortgage lending: article two

Credit scoring systems ; Mortgages

Spatial Models or Random Forest? Evaluating the Use of Spatially Explicit Machine Learning Methods to Predict Employment Density around New Transit Stations in Los Angeles

The increasing use of “new” machine learning techniques, such as random forest, provides an impetus to researchers to better understand the role of space in these models. Thus, this article develops an approach for constructing spatially explicit random forest models by including spatially lagged variables to mirror various spatial econometric specifications in order to test their comparative performance against traditional spatial and nonspatial regression models for predicting block-level employment density around new transit stations in Los Angeles. This article employs a “post hoc” testing approach to isolate the impact of a particular variable (transit proximity)—and supplemental diagnostics (such as partial dependence plots and permutation importances)—to help inform explanatory relationships. The results indicate that random forest models slightly outperform spatial econometric models, and the inclusion of spatial lag parameters modestly improves random forest model accuracy—the best-fit spatial random forest model demonstrates 84.61% accuracy in predicting post-construction employment density around newly built transit stations, compared to 81.88% for the best-fit spatial econometric model and 84.37% for the nonspatial random forest model. However, given these somewhat small differences, it is not possible to conclude that the random forest approach is clearly superior to traditional spatial econometric models from these results alone