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New Insight on Fiscal Impact Analysis

By Dr. Sidney Wong, Project Lead of Community Data Analytics.

On October 18, 2016, along with ESI Senior Advisor Steven Nelson  and Senior Vice President and Principal Dr. Peter Angelides, I led a continuing professional education session with over fifty Pennsylvania planners and local officials in Allentown at the 2016 Annual Conference of the Pennsylvania Chapter of the American Planning Association. Together we offered new insight into fiscal impact analysis (FIA) process and applications, and how the new generation of demographic multipliers can drastically improve this type of work.

New Insights, New Methods

After Nelson introduced the team and gave an overview of the session, Angelides discussed the FIA process and suggested improvements to address estimation issues. He summarized the process and elaborated on the marginal and average cost situation people encounter using current standards. In his conclusion he recommended a hybrid approach specific to the nature of the proposed development.
steps-of-fia

Reverse Fiscal Impact Analysis

Nelson then introduced the concept of “reverse fiscal impact analysis” which is particularly useful in disaster prone areas to explore the feasibility of property buy-outs and other “retreat” options. While a traditional FIA will project the impacts of a proposed development on its community, reversing the process will show the fiscal impacts of a development being removed from its community. With several case studies of New Jersey coastal towns, Nelson showed how this innovative method could revise and re-purpose FIA, generating valuable analysis and information for elected officials and other decision-makers, when considering strategies for hazard mitigation. These initial studies demonstrate the potential value that a reverse FIA can provide as mitigation policies and programs are developed.

New Generation of Demographic Multipliers

My portion of the session focused on FIA data needs and demonstrated how the 2014 Public Use Microdata Sample (PUMS) records provide the best demographic multipliers for forecasting fiscal impacts, compared to outmoded multipliers prepared in the early 2000s.

I indicated that average household size and school-age children (SAC) ratios declined across different housing types between 2000 and 2014. I also discussed the huge variations of SAC across 16 selected geographic areas in Pennsylvania, the difference being up to ten-fold in the case of two-bedroom multifamily units.

Engaged Discussions

Engaged attendees raised several questions. One centered on of the “bumpiness” of the results of a reverse FIA to reflect short and long-term anticipated cost savings and revenue losses. Nelson suggested that the initial results should be fine-tuned by other means such as key person interviews that would provide ground-truthing, as well as sensitivity analysis, to manage expectations for local governments and residents.

Similarly, Angelides stressed the importance of checking each broad expenditure category to determine whether a marginal approach is needed by adjusting the per-capita expense estimation because the increase of expenditures of some functions is not linear.

The conversation also alluded to the complexity of the PUMS records and over dozens of estimation weights involved in generating demographic multipliers. Going through almost 500 variables and 900,000 records for Pennsylvania and developing computer models to mass produce statistically valid multipliers took a team of seasoned data users several months.

For more information about our cutting-edge demographic multipliers and the technical approach for using PUMS records, check out our blog post here. We also invite you to visit our CDA web page, cda-esi.com for a variety of demographic multipliers and planning ratios. You can also view our slideshow “Fiscal Impact Analysis: New Methods, New Data and Best Practices”.

 

 

Sidney-Wong2-e1386020843940

Sidney Wong, Ph.D. is a Senior Advisor with Econsult Solutions, Inc., a fiscal impact expert and the project lead of Community Data Analytics. He previously worked as a senior consultant with the World Bank in evaluating the quality of Project Appraisal Documents in the South Asia Region.


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