August 2014 Pending Home Sales Soft, Remain in Contraction Year-over-Year

September 29th, 2014
in aa syndication, home sales and home prices

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The National Association of Realtors (NAR) seasonally adjusted pending home sales index for August 2014 degraded relative to last month.  Pending home sales index level is down from a year ago, and our analysis suggests that September 2014 existing home sales will be much worse than August's - and will continue to contract year-over-year.

Follow up:

 

  • Even though this was a soft month, the current trends (using 3 month rolling averages) improved from the rolling averages one year ago in pending home sales - but continue to be in contraction.
  • Extrapolating the unadjusted data to project September 2014 existing home sales, this would be a 8.2% contraction year-over-year for existing home sales.
  • Pending home sales are based on contract signings, and existing home sales are based on the execution of the contract (contract closing).
  • After 28 months of year-over-year growth, pending home sales according to the unadjusted data contracted year-over-year for the eleventh month in a row.

The NAR reported August 2014 pending home sales index was down 1.0% month-over-month and down 2.2% year-over-year. The market was expecting month-over-month growth of -2.5% to 4.0% (consensus 0.8%) versus the growth of -1.0% reported. Econintersect‘s evaluation of the unadjusted data shows the index growth decelerated 1.2% month-over-month and down 4.1% year-over-year.

Unadjusted 3 Month Rolling Average of Year-over-Year Growth for Pending Home Sales (blue line) and Existing Home Sales (red line)

/images/z pending2.png

From Lawrence Yun , NAR chief economist:

.... contract signings are holding steady and fewer distressed sales and less investor activity is likely behind August’s modest decline. Fewer distressed homes at bargain prices and the acknowledgement we’re entering a rising interest rate environment likely caused hesitation among investors last month. With investors pulling back, the market is shifting more towards traditional and first-time buyers who rely on mortgages to purchase a home. First-time buyer participation should gradually improve despite tight credit conditions and the inevitable rise in rates. The employment outlook for young adults is brightening and their incomes finally appear to be rising. Jobs and income gains will help repay student debt and better position first-time buyers, setting the stage for improved sales growth in upcoming years.

The National Association of Realtors (NAR) pending home sales index offers a window into predicting existing home sales. The actual home sale might appear in the month the contract was signed (cash buyers can close quickly), or in the following two months.

Econintersect evaluates by offsetting the index one month to project unadjusted existing home sales. Using this index offset one month suggests unadjusted existing home sales of 392,000 in September 2014 (a negative 26,000 fudge factor this month for historical error using this methodology for the month of September's in years past).

Using Pending Home Sales to Predict Existing Homes Sales – Unadjusted Existing Home Sales (blue line) & Predictive Forecast Using Pending Home Sales Index (red line)

/images/z pending1.PNG

Using this methodology, 503,000 (with a positive 73,000 fudge factor), existing home unadjusted sales were forecast for August 2014 sales vs the actual reported number of 479,000 (which is subject to further revision).

Unadjusted Year-over-Year Change in Existing Home Sales Volumes (blue line), 3 month rolling average (red line)

/images/z existing1.PNG

As shown on the above graphic, since mid 2011 home sales have been positively growing year-over-year. However, Since November 2013 home sales showed a contraction year-over-year for the first time since 2011.

Keeping things real – home sales volumes are only 2/3rds of previous levels.

Caveats on the Use of Pending Home Sales Index

According to the NAR:

NAR’s Pending Home Sales Index (PHSI) is released during the first week of each month. It is designed to be a leading indicator of housing activity.

The index measures housing contract activity. It is based on signed real estate contracts for existing single-family homes, condos and co-ops. A signed contract is not counted as a sale until the transaction closes. Modeling for the PHSI looks at the monthly relationship between existing-home sale contracts and transaction closings over the last four years.

…… When a seller accepts a sales contract on a property, it is recorded into a Multiple Listing Service (MLS) as a “pending home sale.” The majority of pending home sales become home sale transactions, typically one to two months later.

NAR now collects pending home sales data from MLSs and large brokers. Altogether, we receive data from over 100 MLSs & 60 large brokers, giving us a large sample size covering 50% of the EHS sample. This is equal to 20 percent of all transactions.

In other words, Pending Home Sales is an extrapolation of a sample equal to 20% of the whole. Econintersect uses Pending Home Index to forecast future existing home sales.

Econintersect reset the forecasting of existing home sales using the pending home sales index coincident with November 2011 Pending home sales analysis (see here) – as the NAR in November revised the historical existing home sales data.

The Econintersect forecasting methodology is influenced by the speed at which closings occur. When they slow down in a particular period – this method overestimates. The number of cash buyers are speeding up the process (cash buyers analysis here). A quick cash home sale process could begin and end in the same month. On the other hand, contracts for short sales can sometimes take months to close. Interpreting the pending home sales data is complicated by weighing offsetting effects in the current abnormal market.

Please note that Econintersect uses unadjusted data in its analysis.

Econintersect determines the month-over-month change by subtracting the current month’s year-over-year change from the previous month’s year-over-year change. This is the best of the bad options available to determine month-over-month trends – as the preferred methodology would be to use multi-year data (but the New Normal effects and the Great Recession distort historical data).

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