Targeting NSP Funds靶向NSP基金.ppt

Targeting NSP Funds靶向NSP基金
Overview
Targeting Requirements in NSP
HUD’s NSP Allocation Formula
Statewide allocation
in other data sets.
Bad news – the data sets were not a perfect match; none had 100% coverage, with varying levels of state and regional coverage.
Most complete data set for all variables required by statute was Mortgage Bankers Association National Delinquency Survey
Statewide Allocation (2)
MBA National Delinquency Survey Data
Statewide data only
Only reflects 70% of active mortgages in US, so HUD adjusts its mortgage counts to prevent potential bias due to coverage problems by adjusting counts of mortgages with Census American Community Survey (ACS) and Federal Reserve Home Mortgage Disclosure Act (HMDA) data.
USPS Vacancy Data in HMDA High Cost Census Tracts
Calculate a measure of “abandonment risk”
Statewide Allocation (3)
Within State Allocations (3)
Model 18 Month Foreclosure Rate =
-
- ( * Percent decline in home value)
+ ( * Pct of loans high cost)
+ ( * Pct unemployed in place or county)
Number foreclosures = model foreclosure rate * estimated number mortgages in jurisdiction (based on HMDA and ACS data)
With a pro-rata adjustment to match aggregate of model number of foreclosures and mortgages to statewide numbers.
Within State Allocations (4)
The needs based sub-allocation formula is:
(Statewide allocation - $19,600,000) *
Local model number foreclosure starts *
Statewide total number of foreclosure starts
Local vacancy rate in tracts with high rate high cost loans
Statewide vacancy rate in tracts with high rate high cost loans
Pro-Rata Adjustment
*
Why no funding for Merced City?
Merced City is an entitlement
Estimated % foreclosure start rate (much higher than state average of %)
But, relatively small number of foreclosures relative to state total, 1,260 estimated foreclosure starts = % of all foreclosures in state.
And only % of its addresses