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Reasonable Rent Determination Comparability Study Summary. Janell Hoppe – National Manager EZ-Reasonable Rent Determination a Division of. Topics. Compliant Data Collection Data Collection Strategy Collect Comparables Unit Data Assign Rental Market Value (RMV) Collected Data Verification

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Reasonable rent determination comparability study summary

Reasonable Rent Determination Comparability Study Summary

Janell Hoppe – National Manager

EZ-Reasonable Rent Determination

a Division of


Topics
Topics

  • Compliant Data Collection

  • Data Collection Strategy

  • Collect Comparables Unit Data

  • Assign Rental Market Value (RMV)

  • Collected Data Verification

  • Quality Assurance

  • Comparability Study Statistics

  • Questions and Answers


Compliant data collection
Compliant Data Collection

24 CFR Section 982.507, Rent to Owner: Reasonable Rent, Section 985.3, (b) of the SEMAP rule, Notice PIH 2003-12,  Notice PIH 2009-51, Notice PIH 2010-18

  • Use only unassisted units

  • Consider all HUD characteristics – location, unit size, unit type, quality, age, amenities, housing services, maintenance, utilities provided by the owner


Data collection strategy
Data Collection Strategy

  • CMHA provided Voucher Holder data by zip code

    • Those zip codes currently under lease

    • Mobility areas that might offer enhanced opportunities for Participant self-sufficiency


Data collection strategy1
Data Collection Strategy

  • Nelrod developed data collection strategy within voucher holder zip codes

    • Ratio matching – Example

      10% of voucher holders located in zip 45205; 10% of comps in 45205

      10% of units are single family units; 10% of comparables will be single family units

      (Comp ratios subj. to availability)


Collect comparables unit data
Collect Comparables Unit Data

Start Comparables Collection

Various data sources used to identify comps

  • Property Manager/Real Estate Marketing Websites

  • Local Newspaper/Craigslist Ads

  • For Rent Signs

  • Local Landlords at CMHA Community Meeting


Collect comparables unit data1
Collect Comparables Unit Data

Data Collected

  • Unit address

  • Landlord information

  • Unit quality (based on HQS)

    • Excellent-exceeds HQS

    • Good-meets HQS w/upgrades

    • Fair-barely meets HQS or minimum repairs needed

    • Poor-many repairs needed


Collect comparables unit data2
Collect Comparables Unit Data

Data Collected

  • Age

  • Unit Type

  • Amenities ( A/C, carpeting, appliances, fireplace, community pool, etc)

  • Facilities (community pool, off-street parking, storage, etc)

  • Housing Services (package receiving, etc)

  • Maintenance (onsite, offsite, poor)


Collect comparables unit data3
Collect Comparables Unit Data

Data Collected

  • Utilities included in rent

  • Rent amount (actual vs proposed when available)

  • Size (includes sq. feet when available)

  • Number of bedrooms

  • Number of bathrooms


Assign rental market value rmv to collected comparables
Assign Rental Market Value (RMV) to Collected Comparables

  • View1-3 block radius surrounding comp. unit

    • High RMV – Above average neighborhood includes:

      • New construction

      • Luxury communities

      • Community amenities such as golf courses

      • State of the art systems

      • Modern appliances


Assign rental market value rmv to collected comparables1
Assign Rental Market Value (RMV) to Collected Comparables

  • Medium RMV – Average neighborhood includes

    • Intermediate community (slightly less favorable than luxury communities)

    • Newer larger homes

    • Community amenities such as community pool/fitness center

    • Quality finishes, adequate systems and appliances


Assign rental market value rmv to collected comparables2
Assign Rental Market Value (RMV) to Collected Comparables

  • Low RMV –Minimal, depleted or impoverished communities

    • Minimal

      • Older, smaller homes in good condition (starter-homes)

      • Community amenities such as parks

    • Depleted or Impoverished

      • Much older communities

      • Large amount of crime

      • Homes may be in bad physical condition, abandoned or vandalized


Collected data verification
Collected Data Verification

  • Off-site Verifications

    • Landlord phone call/email

    • Property tax records

    • Satellite imagery


Collected data verification1
Collected Data Verification

  • Onsite Verifications

    • County-wide neighborhoods tour of zip code areas

    • Performed approximately 350 onsite unit assessments


Quality assurance
Quality Assurance

  • Identify/Delete Anomaly Comparability Units

    • Very high rent

    • Very low rent

    • Unit values unequal to requested rent


Comparability study statistics
Comparability Study Statistics

  • 1005 comparable units as of May 2010 included both vacant and occupied units

  • Beginning June 2010 add additional 35 comps per month for 11 months


Comparability study statistics1
Comparability Study Statistics

  • Comps by Bedroom Size

    • Efficiencies – 4%

    • 1 Bedroom – 33%

    • 2 Bedroom – 28%

    • 3 Bedroom – 25%

    • 4 Bedroom – 8%

    • 5 Bedroom – 2%

    • 6 Bedroom – 01%


Comparability study statistics2
Comparability Study Statistics

  • Comps by Structure Types

    • Garden walkup/multi family- 64%

    • Townhouse - 6%

    • Rowhouse – 2%

    • Duplex – 0.1%

    • Highrise – 4%

    • Single family units – 24%


Comparability study statistics3
Comparability Study Statistics

Zip code containing highest % of high RMV

45208


Comparability study statistics4
Comparability Study Statistics

Zip codes containing 60% or greater medium RMV

45241, 45248, 45255


Comparability study statistics5
Comparability Study Statistics

Zips codes representing deconcentration and/or expanding housing opportunities RMVs

45208, 45241, 45248, 45255


Comparability study summary
Comparability Study Summary

Zip codes containing relatively even match of medium and low RMVs (ranged from 40% to 60%)

45213, 45219, 45239, 45230 and 45242


Comparability study statistics6
Comparability Study Statistics

Zip codes containing the highest % of low RMVs

45204, 45205, 45207, 45211, 45214, 45216, 45223, 45224, 45225


Comparability study statistics7
Comparability Study Statistics

Zip codes that had a significant % of high, medium and low RMVs (may offer widest selection of potential HCVP units)

45202, 45215, 45220, 45227, 45236, and 45246