HomeMy WebLinkAbout2019-09-11_07_30_AM-Advisory_GroupsAgenda
Housing Strategy Task Force
City of Edina, Minnesota
Community Room - City Hall
Wednesday, September 11, 2019
7:30 AM
I.Call To Order
II.Attendance
III.Approval Of Meeting Agenda
IV.Approval Of Meeting Minutes
a.Minutes: August 30, 2019
V.Discussion Items
a.Metropolitan Council Presentation
VI.Adjournment
The City of Edina wants all residents to be comfortable being part of the public process. If
you need assistance in the way of hearing ampli*cation, an interpreter, large-print
documents or something else, please call 952-927-8861 72 hours in advance of the meeting.
Date: September 11, 2019 Agenda Item #: IV.a.
To:Members Item Type:
Minutes
From:Danielle Boschee
Item Activity:
Subject:Minutes: August 30, 2019 Action
CITY OF EDINA
4801 West 50th Street
Edina, MN 55424
www.edinamn.gov
ACTION REQUESTED:
Approve August 30, 2019 meeting minutes.
INTRODUCTION:
None.
ATTACHMENTS:
Description
August 30, 2019 Minutes
Draft Minutes☒
Approved Minutes☐
Approved Date: Click here to enter a date.
I. Call To Order
Chair Hornig called the meeting to order at 7:35 AM
II. Roll Call
Present: Co-Chairs Hornig and Hunt; Members Burke, Koon, Kitui, Mehta and Siekman; Staff
Liaison Hawkinson and Staff Boschee
III. Approval Of Meeting Agenda
Agenda was accepted as presented. Siekman moved; Kitui seconded.
IV. Approval Of Meeting Minutes
Kitui moved; seconded by Siekman, to approve the July 31 and August 9 meeting minutes.
Motion carried.
V. Discussion Items
• Housing Strategy Task Force timeline approved to be extended through December
31, 2020; will be mentioned in council meeting to ensure public is aware
• Fleshing out community involvement
• Maxfield Study completion at end of November
• Future HSTF meeting dates and calendar scheduling
VI. Adjournment
The meeting ended at 8:35 a.m.
Minutes
Housing Strategy Task Force
City Of Edina, Minnesota
Community Room
August 30, 2019 7:30 AM
Date: September 11, 2019 Agenda Item #: V.a.
To:Members Item Type:
Advisory Communication
From:Stephanie Hawkinson, Affordable Housing
Development Manager Item Activity:
Subject:Metropolitan Council Presentation Information
CITY OF EDINA
4801 West 50th Street
Edina, MN 55424
www.edinamn.gov
ACTION REQUESTED:
None.
INTRODUCTION:
Tara Beard from the Metropolitan Council will give a brief presentation and discuss the role of the Met Council
as it relates to the Comprehensive Plan.
ATTACHMENTS:
Description
Power Point Presentation
Met Council Handout
Edina Housing Task Force
September 11,2019
1.How does the Council forecast growth?
2.How does the Council allocate affordable housing need?
3.Are cities meeting their previous affordable housing allocation?
4.Affordable housing definitions and measures
5.Can the private market create affordable housing?
6.What is the Council’s approach toward investment in ACP and ACP50s?
7.What does the Council do to encourage affordable housing?
8.Best practices, locally and nationally
Overview of today’s presentation
2
How does the Council forecast growth?
REMI PI, a regional economic model
•Projects economic activity and demographic outcomes
•Informed by data on the region’s industry mix, costs and productivity, and analysis of
regional competitiveness within the national economy
•To obtain household counts, the REMI PI population projection is divided into household
types using race-specific, age-specific household formation rates from analysis of U.S.
Census data
Regional need
Local adjustments
•Forecasted growth
•Existing affordable housing
•Ratio of low-wage jobs to low-wage workers
Local need by affordability band
•Below 30% AMI
•31-50%of AMI
•51-80%of AMI
How does the Council allocate affordable housing
need?
4
Are cities meeting their current affordable
housing allocation?
Cities that have met at least 70% their
2011-2020 allocation of affordable
housing need (as of 2017):
•Brooklyn Center
•Champlin
•Columbia Heights
•Crystal
•Excelsior
•Mahtomedi
•South St. Paul
•West St. Paul
Cities that have met at least 70% their
2011-2020 Livable Communities Act goal
(as of 2017):
•Brooklyn Center
•Champlin
•Columbia Heights
•Crystal
•Excelsior
•Hopkins
•Mahtomedi
•New Brighton
•Newport
•South St. Paul
•West St. Paul
Housing is considered
“affordable” if it costs less than
30% of a household’s gross
income –more or less
What is “affordable”housing?
All other
household
expenses:
Food
Transportation
Child Care
Health Care
Taxes
6
Housing
Costs
Which households are “low-income”?
Source:U.S. Department of Housing and Urban Development.
Based on family of four in 2018
$94,300
7
$71,900
$56,580
$47,150
$28,300
Area Median
Income (AMI)
Households with
income at 80%AMI
60%AMI
50%AMI
30%AMI
Since 2011, the Council uses
the term “affordable” to
describe housing units that
low-income households could
pay for with up to 30% of their
monthly income. Low-
income households are
those with incomes up to
60%AMI.$13.60 /hour
$22.67 /hour
$27.20 /hour
What can low-income households “afford”?
$991 $1,062
$1,273
$1,471
$1,640
$495 $531 $636 $735 $820
Efficiency 4 BR
Monthly Rents
1 BR 2 BR
60%AMI
3 BR
30%AMI
$234,500
8
$181,500
$149,000
$83,500
80%AMI 60%AMI 50%AMI 30%AMI
Home Prices
Sources:U.S. Department of Housing and Urban Development and Metropolitan Council, 2018
Can the private market create affordable
housing?
9
1.Is market driven affordable housing production possible?
•Short Answer: no
•Cost to build/operate > what affordable rents can support
2.How can it become easier/more possible?
•Larger employer presence in the housing market
•Regulatory reform to allow more manufactured, modular, tiny and other alternative types of housing
•Let luxury demand plateau
•Reduce the costs of production?
3.What can be done to reduce the cost of housing production?
•Land Costs = 10-15% of total development costs
•Soft Costs = 13-17% of total development costs
•Hard Costs = 70-80% of total development costs
What is the Council’s approach toward
investment in ACP and ACP50s?
•Where we prioritize ACP/ACP50 investment
•Regional solicitation scoring
•TBRA funding
•Where we prioritize affordable housing investment in areas of wealth
•Local Housing Incentives Account applications
Best practices
In the Twin Cities:
•Bloomington: Opportunity Housing Policy
•St. Louis Park: Tenant protections
•Minneapolis: Section 8 discrimination
In the country
•California: Anti-NIMBY laws
•Washington DC: Tenant opportunity to purchase
•Seattle: Allowing smaller units
9
How does the Council encouraging affordable
housing?
•Providing rental assistance to low-income households
•Providing data and analysis to understand regional
trends, both current and forecasted
•Reviewing local comprehensive plans
•Funding housing development through the Livable
Communities Act programs
•Providing incentives for housing performance
•Providing technical assistance and support to local
governments
•Collaborating with and convening partners and
stakeholders to expand the regional housing dialogue
The Moline, a market-rate housing, benefited from a Livable Communities
Demonstration Account to the City of Hopkins to develop The Artery.
Tara Beard
Housing Policy Analyst
Tara.Beard@metc.state.mn.us
651-602-1051
Discussion
13
METROPOLITAN COUNCIL'S
FORECASTS METHODOLOGY
JUNE 14, 2017
IEOTRL PN° 16i TANI L
Metropolitan Council's
Forecasts Methodology
Long-range forecasts at Metropolitan Council are updated at least once per decade. Population,
households and employment levels are projected with a 30-year time horizon. The regional and local
forecasts express future expectations based on an understanding of regional dynamics, and modeling
of real estate market dynamics, land policies and plans. Consistent with Minnesota Statutes 473.146
and 473.859, the Council's forecasts provide a shared foundation for coordinated, comprehensive
planning by the Council and local governments.
A regional forecast and local forecasts were included in the Thrive MSP 2040 regional plan, adopted by
Metropolitan Council on May 28, 2014.
These forecasts were subsequently updated and improved.
• Biennial updates to the regional model, in 2015 and 2017, included: update of the national
economic and employment forecast; updates of all time-series with the most recent year of
historical data; model vendor's programming improvements and recalibration.
• The 2015 update to local forecasts included: use of updated and more detailed planning data;
use of more locally detailed land supply analysis; revision of land consumption rates; input of
more detailed data on residential building costs and real estate prices (rents); updated
transportation network definition and accessibility data.
Overview of forecasting project.
Metropolitan Council's regional forecast considers the Twin Cities' situation within the larger, national
economy: An analysis of regional economic competitiveness determines forecasted employment levels,
which in turn prompt population growth through migration.
Subsequent to the regional forecast, local forecasts address the likely geographic pattern of future
growth. Regional population, households and employment will site in specific places. Metropolitan
Council assumes that real estate and land market dynamics, interacting with future transportation
accessibility, primarily determine outcomes, shaped by land use policies and local plans.
Considering the multi-scale nature of future planning needs, Metropolitan Council employs multiple
forecast modeling tools:
A regional economic model for forecasting region-level economic activity and migration flows in
response to economic opportunity.
A land use model simulating and projecting real estate and land market dynamics, in order to
locate future land use, households and employment to communities and zones.
• A travel demand model for predicting modes, network paths and network conditions.
• A hydrogeologic model for projecting water demands and water resource impacts.
This document addresses the first two models.
Page 12
Methodology of REMI PI.
Following a review of best practices in regional economic modeling, the Council selected REMI PI as
the model best fitting the Council's understanding of regional growth. REMI PI is a structural
macroeconomic simulation model. It utilizes computable general equilibrium techniques to project
forward time-series of economic activity, as well as input-output matrices to represent inter-industry
flows and impacts. Also, the model employs new economic geography techniques to represent trade,
migration flows, and other aggregated interactions among regions.
Simulation and projection of economic activities (production, consumption, and trade) are central to the
model. Macroeconomic functions determine the balance of capital, and labor levels; and the model
seeks equilibrium between industries' labor demand, wage levels, and labor supply.
Population changes are projected simultaneously using detailed cohort-component demographic
techniques to project fertility, births, aging and survival rates, and new economic geography techniques
to project labor market results and migration. If industries' labor demand intensifies (or slackens), then
labor supply adjusts up (or down) through migration. Thus, economic competitiveness and labor
demand are the major determinants of migration in the REMI PI model.
A more detailed description can be found in the model documentation:
• Regional Economic Models Inc. (2017), REMI Pl+ Model Equations, online at
www.remi.corn/download/model-equations-v2-1?wpdmd1=8505
Our Minnesota implementation of the model has two home regions: the Twin Cities 7-county metro is
one; the balance of Minnesota is a second region; the rest of the nation and the world are additional
linked economies.
The model delivered by Regional Economic Models Inc. assesses the Twin Cities metro having factor
cost advantages, resource advantages, and breadth of workforce supply. The model also assesses
finds under-performance in noneconomic attraction of population. In periods of economic expansion,
the region has experienced, and may continue to experience, workforce supply shortages. These
characteristics inform a forecast of slightly above-average growth in coming decades. Metropolitan
Council forecasts that the Twin Cities metro will account for 1.3 percent of national GDP in 2040.
Modifications to the as-delivered REMI PI model.
In the implementation of REMI PI, the Council modifies some settings and data inputs to the "as
delivered" model. First, the national forecast in the Council's model is controlled to match nation-level
GDP projections from IHS Global Insight's 30-year Trend forecast; this is the same forecast used by the
Minnesota State Economist as a baseline for long-term, national economic expectations. The national
forecast is significant insofar as the Twin Cities metro region's growth is tethered to national economic
conditions. For more information, see:
• Minnesota Management & Budget (2017, and updated bi-annually), Minnesota and U.S.
Economic Outlook, online at http://mn.qov/mmb/forecast/forecast/
Second, the Council updates regional time-series with observed actuals:
• Fertility rates schedules (fertility rates by race and by age of mother) are re-leveled so that the
base year matches region-specific rates calculated from the most recent 5 years of births data
tracked by Centers for Disease Control. In the Twin Cities metro, the 2011-15 total fertility rate
for whites is less than 1.7 children per woman; the rate for blacks is 3.0; the rate for Latinos is
2.2; the rate for Asians and other race groups is 1.9.
Page 13
• The most recent two years of industry employment statistics are updated with data from
Minnesota Department of Employment and Economic Development.
Model vendor-provided assumptions and data are reviewed and modified as necessary. There are
variables in the model that are recognized as difficult to project. Generally, the Council assumes a
stable status quo or median values within the range of possibilities. Specifically:
• College-going population by race is projected to increase in tandem with growth in the resident
population of 17-year-olds by race.
• The balance of long-distance commuters into the region and out of the region is adjusted for
future years, in order to better fit the observed trend. This adjustment effectively holds constant
the rate of reverse commuting (living in MSP but commuting to Greater Minnesota).
A few model vendor-provided projections that have sometimes needed adjustment, but are not adjusted
in the latest modeling, include the following:
• Tax rates for the Twin Cities and Minnesota are projected to remain level.
• Regional consumer prices relative to the national average are not adjusted in the latest
modeling. In previous forecast updates, Metropolitan Council modified the REMI projection of
Minnesota fuel prices to mitigate unexplained deviance from national average prices.
• Regional average housing prices relative to the national average is projected to remain in the 94
to 100 percent range throughout the forecast period. In previous forecast updates, Metropolitan
Council made adjustments to mitigate unexplained drops in the relative housing price.
The forecast models described above provide details on future demographics and industry composition
at a macro-level, without local geographic detail. To obtain household counts, the REMI PI population
projection is parsed into household types using age-specific household formation rates obtained from
analysis of Census American Community Survey data.
Additional modeling, at a local scale, is necessary to project the geographic distribution of households
and industries' employment over time.
Methodology of Cube Land.
In 2009, Council staff conducted an internal needs assessment and a state-of-the-practice review of
land use models. Council staff recommended adoption of a market simulation model capable of
producing zonal projections of households, population and employment, as well as accounting future
land use. In 2010, the Council licensed and implemented Citilabs Cube Land as a platform for local real
estate and land market modeling and scenarios analysis. Cube Land was chosen in part for its potential
to integrate with the Council's travel demand model, allowing land use patterns and transportation
network conditions to iteratively adjust Over time.
The logic of Cube Land is the market sorting and equilibration of real estate demand and supply, and
the addition of new supply, assuming best-use and value-maximizing decisions of site selectors,
developers and households. Cube Land assumes that developers will build in places where households
or firms find value, where that value exceeds costs of construction and land, and where policies and
land capacity allow for development. Cube Land includes three submodels:
• The demand submodel simulates an auction in which different market segments are willing to
pay differential amounts for combinations of real estate and place characteristics.
• The rent submodel uses estimated bids, along with other local characteristics, to estimate rents
for different real estate types at specific locations.
Page I4
• The supply submodel projects forward real estate development by comparing rents with supply
costs, and locating new development based on estimated profits (rent minus supply costs) and
land supply availability.
In summary, households and worksites choose real estate in specific locations, so as to maximize
value. Developers respond by supplying real estate responsive to the demand.
The demand model mathematically represents the preference structures of different household market
segments and industry sectors using variables, and parameters for variables, identified and estimated
through discrete choice analysis of existing behavior (known through survey data). Variables include
neighborhood characteristics and accessibility to destinations. These quantified preferences allow the
model to estimate probabilities of all potential real estate choices for each defined household type and
worksite type. The location options correspond to the post-2000 Transportation Analysis Zones (TAZs)
used in the Council's travel demand model.
Many of the variables that determine the choice probabilities can change over time: Summarized land
use and remaining available land supply, industry mix, and socioeconomic mix of zones are projected
and updated within the model. Accessibility measures are projected and updated through iterative
looping with a linked travel demand model.
Concurrently, the rent model uses estimated bids, as well as other zonal characteristics, to calculate
and update rents within the model. If real estate and land in a certain location are highly desirable to
one or more market segments, rents can change, altering estimated distributions (or probabilities) of
household and worksite location choices, and prompting choice substitution. Ultimately, the model
seeks an equilibrium solution where all forecasted future households and employment are sorted into
locations, proportionate to updated choice probabilities.
The discussion above concerns different market sectors valuing locations, and sorting themselves to
accomplish best-value results. Importantly, Cube Land allows supply response to growing and
changing market demand. To accommodate growth in households and employment — which has been
forecasted using the region-level forecast models — the Cube Land supply submodel projects the
addition of new housing and employment-bearing built space. In the Twin Cities implementation of
Cube Land, the major determinants of such development are land supply and estimated rents for each
zonal location. As rents are dynamically estimated within the model, the geographic distribution of new
development is likewise dynamic — with new growth precipitated by lower development costs and/or
higher rents for valued location characteristics.
Data and Variables Used in the Council's Cube Land Modeling
The Twin Cities implementation of Cube Land segments worksites and employment into 8 industry
sectors; these groups have varying preferences and use varying amounts of 5 types of employment-
bearing real estate. Households are segmented by socioeconomic characteristics into 5 major
household types (and additional subtypes), which then select housing from 8 housing product types.
This segmentation enables moderate representation of how real estate and location preferences vary
among different household and industry types.
The Cube Land system allows flexibility in defining the set of variables that comprise preferences and
valuations of real estate. The variables identified as most significant, and included in the Council's
modeling, are compiled for 1,201 Transportation Analysis Zones. These zonal characteristics also
inform the calibration of the model to year 2010 conditions. Zonal characteristics include:
• Real Estate Characteristics:
Page I 5
o Start-year land use mix and undeveloped land supply
o Existing housing stock and employment-bearing built space
o Average land consumption per real estate unit
o Average building costs and land values
o Average real estate prices (rents)
• Surrounding Land Uses:
o Proximity to lakes and rivers
o Zonal demographics
o Zonal employment
o Housing density
• Regional Systems and Services:
o Proximity to parks
o Wastewater service availability
o High frequency bus stops and LRT stations
• Transportation Accessibility, obtained through interaction with the Council's travel demand
model:
o Number of jobs within 20-minute travel time (by automobile and by transit)
o Number of households within 20-minute travel time (by automobile and by transit)
The Cube Land model also uses local planned land use and regional policies when forecasting future
real estate and land supply, including:
• Planned land use acreage (from analysis of local comprehensive plans)
• Allowable real estate types
• Existing housing densities
• Maximum housing capacities and densities (from local comprehensive plans)
Several of the dataset inputs listed above were revised and improved in 2015. Most notably, Council
staff calculated maximum housing capacities using more locally detailed data and a conservative
assumption that housing growth will be restricted to sites that are currently undeveloped or under-
utilized (under-built) relative to local land prices. In summary, the Cube Land model is richly informed
about base year conditions and the envelope of future possibilities.
Model maintenance and forecast updates.
Metropolitan Council receives annual updates of the REMI PI software and time-series data inputs. The
model received in May 2017 includes time-series data for years 2001-2015, as well as national
demographic adjustments to reflect US Census Bureau's immigration assumptions from 2014.
For more information on national projections, see:
• US Census Bureau (2014), National Population Projections, online at
www.census.gov/population/projections/data/national/
A regional forecast and local forecasts were included in the Thrive MSP 2040 regional plan, adopted by
Metropolitan Council on May 28, 2014.
These forecasts were subsequently updated and improved.
Page 16
Biennial updates to the regional model, in 2015 and 2017, included: update of the national
economic and employment forecast; updates of all time-series with the most recent year of
historical data; model vendor's programming improvements and recalibration.
The 2015 update to local forecasts included: use of updated and more detailed planning data;
use of more locally detailed land supply analysis; revision of land consumption rates; input of
more detailed data on residential building costs and real estate prices (rents); updated
transportation network definition and accessibility data.
The Council adopted an updated set of local forecasts in July 2015, and approved these for use in
Council system plans. For this work, geographic representation of regional policies has been limited to
a base-case scenario, including: the Metropolitan Urban Services Area, defining the coverage of
wastewater service in 2040; the 2040 regional transportation network, incorporating the planned, long-
term program of transitways and highway improvements to 2040; and planned land use from local
prepared by communities during 2005-2014. The planned land use data may not yet include land that
will be guided for development during 2031-40.
Page 7
METROPOLITAN
COUNCIL
390 Robert Street North
St Paul, MN 55101-1805
651.602.1000
TTY 651.291.0904
research@rnetc.state.mn.us
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