States, municipalities, and school districts make land use decisions that influence their fiscal and economic conditions. Understanding the effects of those decisions on schools, traffic and municipal services can help to prevent school overcrowding, gridlock, gaps in services and fiscal distress. A fiscal impact analysis (FIA) is an important tool for assessing how new development will impact costs and revenues, helping leaders make better decisions for their respective communities. Unfortunately, many FIAs are based on data that is out-of-date and lacks sufficient geographic granularity to support sound planning decisions.
At ESI, we have conducted numerous FIAs. For years, like other practitioners, we have been relying on the multipliers compiled by Professors Burchell and Listokin from Rutgers University. The main two multipliers used to estimate occupants’ characteristics are the average household size (AVHH) and school-aged children (SAC). Each is reported at the state level or multistate region by housing configurations (categories combining number of bedrooms, building structure, and whether the unit is rented or owner-occupied). These multipliers were calculated from the Census Public Use Sample (now Public Use Microdata Sample or PUMS).
The most recent Rutgers multipliers, released in 2006, though, are based on the 2000 PUMS that is essentially a dataset from the 1990s. People would not consider the beeper to be representative of mobile phone technology today. The same can be said for the census data from 1990, for it is not representative of our demographics today. Just as we rejoice in the improvements in our standards of technology, we must take active steps to improve and updated our demographic datasets. The standard data used is outdated and disconnected from the local demographic realities, which has serious repercussions on the estimations produced. Our world has changed in tremendous ways and the data must reflect these changes. Similarly, Upper and Lower Merion are not the same place. Northern Philadelphia and Center City are not the same place either. The data must be representative of distinct regions and neighborhoods.
Facing the problem of the lack of updated multipliers, we took the matter into our own hands and started to work on a better approach. We decided to examine current PUMS and explored if it is possible to generate planning ratios at the sub-state level. Now, PUMS is released every year and provides records of individual residents, households, and housing units at a geographical unit covering approximately 100,000 persons.
Showing the Disconnect
Let us take a look at inaccuracies produced with the outdated multipliers versus our approach.
Changing Populations and Demographics
The first problem is that the old multipliers do not reflect the new demographic realities. Between 2000 and 2010, the AVHH varied greatly across states. It declined in 40 states and DC but grew in Nevada, California, Florida, Delaware and Texas. It remained unchanged in New Jersey, Maryland, Virginia, Oklahoma and Tennessee.
Fig 1 Ten States with the Greatest Decline in Average Household Size in comparison to US
Many reasons explain the decline in most states, including falling fertility, later marriage, population aging, and the rise of single-parent families and baby-boomer single-person households. In contrast, states with increasing AVHH, except Delaware, have a sizeable presence of Hispanics.
Figure 2 compares the Rutgers 2-bedroom SAC for New Jersey with those generated from the most current 2014 PUMS. Using the old multipliers would result in 70 percent of overestimation of SAC in single-family detached units and 33 percent underestimation for townhomes.
Fig 2 Estimation Biases in Percent Difference in School-Age Children of Using the 2000 PUMS instead of the 2014 PUMS Generated Ratios: Recently Constructed 2 Bedroom Units in New Jersey by Housing Type
Sources: 2000 PUMS-based ratio –New Jersey Demographic Multipliers: The Profile of the Occupants of Residential and Nonresidential Development, 2006; 2014 PUMS-based ratio, Community Data Analytics, 2016; analysis of 2010-2014 5-Year PUMS data.
Local geographic realities
The second problem is more significant. Ideally, these multipliers should be based on local and geographic specific areas. The 2006 Rutgers multipliers were reported at the state level (with a single exception for New Jersey), masking local differences. We should not assume that sprawling suburbs and urban centers have the same demographics. As well, there are natural cycles of populations moving and out of cities, which would not be properly documented with older data.
Here is an illustration of the variations of SAC between the Lower and Upper Merion area in Pennsylvania, using the updated 2014 PUMS data. Using the same year statewide SAC ratio would grossly overestimate school impacts by two times for 2-bedroom multifamily units in the Lower and Upper Merion area, but underestimate the effects for single family 2-bedroom units.
Fig 3 Comparing the 2014 School-Age Children Ratio between Pennsylvania and Lower and Upper Merion, Newly Moved-In Households in 2-Bedroom Units by Housing Types
Sources: Community Data Analytics, 2016; analysis of 2010-2014 5-Year PUMS data.
The use of the Rutgers dataset leads to a drastic overestimation of statewide ratios versus the actual neighborhood ratios.
The implication is clear. FIA practitioners have to be very cautious in selecting the appropriate dataset to estimate the occupant characteristics of future development. In this regard, old multipliers or statewide figures should be replaced by those generated from current PUMS data.
Better data, better decisions, better community
Our team is currently conducting analysis of the 2014 PUMS and sees tremendous potential for the use of such data when it comes to providing detailed analyses and forecasts of population, household and housing characteristics at the local level. By utilizing the PUMS data, communities can see an accurate depiction of their community, which will help them in making those critical decisions about future developments. Communities can now have better information to know how many school buses they need, what new property developments should be constructed, and what traffic lights are needed to help traffic patterns. Our team has been successfully generated critical ratios other than SAC and AVHH, such as number of cars and average household income by housing configuration. In addition, we can generate these ratios for targeted population groups, such as low and moderate income households, households who commute in public transit, or those with a householder 55 years and older.
Interested in learning more? The CDA research team will make a presentation on June 4, 2016 entitled Projecting Development Impacts for Sustainable and Fiscally Responsible Growth in the National Capital Region Chapter of the American Planning Association annual conference.
Learn more about the work done by the CDA team here.
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.