United States - House Price Value for Existing Homes

From the National Association of Realtors, sales prices (quarterly and counts of sales (monthly) for existing homes (single-family, condo, co-op); for the U.S., the four census regions, and select metro areas; based on Board and MLS data.

• Measurements:
  • Thousands of U.S. dollars (Ths. USD)
  • Thousands of units sold (Ths. #)
• Adjustment: Not seasonally adjusted (NSA)
• Native frequencies:
  • Monthly
  • Quarterly
• Start date:
  • As early as 1968m1
  • MSAs as early as 1981Q1
  • Metro divisions from 1989Q1
• Geo coverage:
  • Country
  • All 4 census regions (C^^R)
  • Select 175 MSAs (2010 delineations)
  • Select 7 metro divisions
  • One county in USVI

Predecessor(s):

• Monthly average price - from (1968m1-1999m1) to 2022m4 (no specifier)
• 172 MSAs, 6 metro divisions, and CMNEY; under 2000 delineations, quarterly - from (1989-2012) to 2016Q1
• 148 MSAs under 1990 delineations, quarterly - from (1970-1989) to 2005Q1

The source writes:

Each month, the Research Division of the NATIONAL ASSOCIATION OF REALTORS® receives data on existing single-family home sales from Boards or multiple listing services (MLS) nationwide. NAR estimates that it captures between 30-40% of all existing home sale transactions with its monthly survey. The data provide the total number of closed existing home sales in each Board/MLS and also total sales within price categories ranging from less than $30,000 at the bottom to more than $500,000 at the top. Participants of the survey are situated in every region of the country and provide wide geographic coverage of the existing home market. While almost all reporting Boards/MLSs are located in, or adjacent to, metropolitan statistical areas, comparisons of their sales with the American Housing Survey (Census Bureau) show that, as a group, their experience is representative of sales activity and prices that generally prevail in each region of the country. The statistics are published for the U.S. and for the four census regions of the country. State volume, which also include sales of condominiums and co-operatives, is based on the entire survey of nearly 700 Boards/MLSs and is reported quarterly to ensure each state receives optimal representation.

In general terms, the methodology in calculating existing home sales statistics is really quite simple. The monthly EHS economic indicator is based on a representative sample of 160 Boards/MLSs. The home sales data (raw data) is divided into the four census regions: Northeast, South, Midwest and West. The raw sales volume from the participating Boards/MLSs is carefully evaluated by NAR economists to ensure accuracy. Some of the possible problems with the data could be caused by: Changes in Board/MLS physical jurisdiction, Changes in MLS vendors and /or staff, Lack of response by Boards, Erroneous data.

Once the outliers or "problematic data" have been extricated from the sample, the aggregated raw figures are weighted to accurately represent sales activity for each region of the country. This is also called the non-seasonally-adjusted volume. The weights are benchmarked every 10 years to reflect shifts in regional demand (For more information on NAR's weighting system refer to rebenchmarking procedures at NAR.reactor.com/research). The non-seasonally adjusted volume is then converted into seasonally-adjusted annualized rates. (See section on Seasonal Adjustments)

Quarterly estimates for total existing home sales by state for single-family homes and condominium/co-op are developed subsequent to the national and regional single-family and apartment condominium/co-op statistics. Constant factors were developed by comparing sales reported by participating Boards/Associations to market activity benchmarks from the 1980 Census of Housing and data obtained from public records, and used in the estimates.

The majority of the information is reported at time of closing. However, some of the sales are reported when the contract is signed. This leads to a certain amount of lag in the data series.

Metropolitan areas

NAR releases median home price, afforadbility index, month's supply and pending home sales statistics for approximately 175 Metropolitan Statistical Areas (MSAs). The areas are defined by the U.S. Office of Management and Budget (OMB Bulletin 18-03). Data is available for existing single-family homes and apartment condo-coops. 

How does the NAR calculate month's supply?

XHX1SUPM.IUSA = XHX1INVM.IUSA / (HX1M.IUSA / 12)

Months supply = Inventory / Sales

Median and mean (average) prices are computed for the nation and four census regions on a monthly basis. Median prices are also calculated for selected metropolitan areas and are reported quarterly to give adequate time for data gathering. Due to the nature of the distribution of home sales prices, the mean sales price is usually higher than the median price. There is a slight degree of seasonal variation in reporting selling prices. Sales prices generally experience the largest gains in the summer months, as favorable weather conditions create an ideal atmosphere for buying and selling a home. Demand for homes usually hits its seasonal peak in the third quarter, and strong price appreciation generally follows suit, and then declines moderately over the next three months. Despite the slight seasonal variances that exist in the price series, sales prices are not seasonally adjusted. The reason for this is that seasonal variances are extremely fickle and difficult to gauge. Furthermore, changes in the characteristics and size of a home have a more pronounced effect on home prices.

Housing affordability

The NATIONAL ASSOCIATION OF REALTORS® affordability index measures whether or not a typical family could qualify for a mortgage loan on a typical home. A typical home is defined as the national median-priced, existing single-family home as calculated by NAR. The typical family is defined as one earning the median family income as reported by the U.S. Bureau of the Census. The prevailing mortgage interest rate is the effective rate on loans closed on existing homes from the Federal Housing Finance Agency and HSH Associates, Butler, N.J. These components are used to determine if the median income family can qualify for a mortgage on a typical home.

To interpret the indices, a value of 100 means that a family with the median income has exactly enough income to qualify for a mortgage on a median-priced home. An index above 100 signifies that family earning the median income has more than enough income to qualify for a mortgage loan on a median-priced home, assuming a 20 percent down payment. For example, a composite HAI of 120.0 means a family earning the median family income has 120% of the income necessary to qualify for a conventional loan covering 80 percent of a median-priced existing single-family home. An increase in the HAI, then, shows that this family is more able to afford the median priced home.

The calculation assumes a down payment of 20 percent of the home price and it assumes a qualifying ratio of 25 percent. That means the monthly P&I payment cannot exceed 25 percent of a the median family monthly income.

Formulas used to calculate HAI:

1. Principle & Interest Payment Monthly - PMT = MEDPRICE*.8 * (IR/12)/(1-(1/(1+IR/12)^360))
   • IR = Interest Rate
   • MEDPRICE = Median price of existing single-family homes (EHS by NAR)
2. Median as % of income (necesarry monthly payment) = ((PMT*12)/MEDINC)*100
   • PMT = Principle & Interest Payment Monthly
   • MEDINC = Median Family Income from Census Bureau American Community Survey
3. Qualifying Income = PMT * 4 * 12
   • PMT = Principle & Interest Payment Monthly
4. Housing Affordability Index (Composite) = (MEDINC/QINC)*100
   • MEDINC = Median Family Income
   • QINC = Qualifying Income

The median starter single-family existing home price is equivalent to 85% of median single-family existing home. Historically, based on the Profile of Home Buyers and Sellers, that has been the ratio of homes purchased by first-time buyers to all buyers.

Moody's Analytics supplements

We compute certain indicators as arithmetic identities of as-reported time series.

We compute seasonally adjusted versions of as-reported and identity time series, using the U.S. Census Bureau's X-13 ARIMA program.

Data are revised monthly for the preceding month. NSA data are revised annually in December for the preceding year and the SAAR data are revised for the preceding years to incorporate new seasonal patterns. Major benchmark revisions are made approximately every ten years.

Benchmark revisions to single-family existing owner-occupied home sales

The benchmark for single-family existing home sales comes in a fairly straightforward manner from the Public Use Micro-sample (PUMS) of the 2000 U.S. Census. Specifically, we have used the "PUMSA" sample, or the 5 percent sample. This sample gives us detailed information on one in twenty housing units enumerated in the 2000 Decennial Census. Because the census draws a stratified (i.e., non-random) sample, each household in the sample receives a weight that reflects how representative it is of the overall population. Altogether, the five percent sample allows us to draw inferences about housing characteristics for the nation based on over 5,000,000 households.

For every household in the sample we determine the following:

• whether it lives in an owner-occupied house
• whether it lives in a house that it moved into during or after 1999
• whether it lives in a single-family attached or detached house
• whether it lives in a house that was built before 1999

Houses that meet all the above criteria are Single-family Owner-Occupied Existing Home Sales for 1999 and the first three months of 2000 (the census does not ask about a particular year). Note what is not included: Mobile Homes, New Homes, Multifamily Homes, and Commercial Properties.

Benchmark revisions to existing condominium and co-operative sales

The procedure for determining existing condominium and co-operative sales is similar to the procedure for existing single-family home sales. For owner-occupied condominium sales, we once again rely on the five percent PUMS data, and find observations that meet the following criteria:

• household responds that it paid a condo/co-op fee
• household responds that it moved in 1999 or 2000
• household responds that unit was built before 1999
• household responds that it does not live in a single-family attached or detached unit (so we do not double count units that are both single-family and condominiums)

For investor and vacant condo sales, we follow the exact same procedure as that for investor and vacant single-family sales, with two exceptions. We look only at the sample of condominiums that are not single-family sales in the Residential Finance Survey, and we only determine regional turnover rates (rather than regional rates plus the rates in the four largest states). The reason for the latter restriction is a practical one—there are not enough observations involving investor and vacant condominium sales for the four individual states to get reliable turnover rates for condominiums in these states.

Each year in October, NAR revises monthly data to incorporate the latest results from the BOC American Community Survey - ACS (family income). NAR also revises data when revisions are made to the BEA personal income data set. This data is used to forecast family income levels later than what is provided by BOC.

Attribution

Data from Real Estate Outlook: Market Trends & Insights, used with the permission of the NATIONAL ASSOCIATION OF REALTORS®. Copyright 1999 NATIONAL ASSOCIATION OF REALTORS®. Such data shall not be redistributed in any way without permission of the NATIONAL ASSOCIATION OF REALTORS®. Information has been obtained by the NATIONAL ASSOCIATION OF REALTORS® from sources believed to be reliable. However, because of the possibility of human or mechanical error by the NATIONAL ASSOCIATION OF REALTORS® or others, NATIONAL ASSOCIATION OF REALTORS® does not guarantee the accuracy, adequacy or completeness of any information and is not responsible for any error or omissions or the results obtained from the use of such information.

Further reading

At the source:

• HAI Methodology
• EHS Methodology