The Home Purchase Sentiment Index

The Home Purchase Sentiment Index:

A New Housing Indicator

James A. Wilcox

Haas School of Business

University of California, Berkeley

Acknowledgements

We thank David Berson, Qiang Cai, Steve Deggendorf, Doug Duncan, James Glassman, 

Hamilton Fout, Mark Palim, Karen Pence, Shane Sherlund, Dan Van Dyke, Mark Winer, and 

seminar participants at FHFA, Fannie Mae, and FRB San Francisco for helpful comments. 

Remaining errors are mine alone.
The Home Purchase Sentiment Index

Abstract

We built Fannie Mae’s Home Purchase Sentiment IndexTM (HPSI) with consumers’ responses to 

questions about home buying and selling conditions, income gains and job concerns, and house 

price and mortgage rate expectations. The HPSI can help forecast mortgage originations and 

house prices, sales, and starts. Forecasting horse races during 2012-0214 show the HPSI handily 

outperformed other sentiment indices. We show differences in the HPSI by income and by age

groups. We also suggest other aspects of housing where survey-based indicators may help, such 

as demand by millennials or seniors, home owners’ moving, renters’ intentions, and mortgage 

refinancing, delinquency, or default rates.

Keywords: housing, sentiment, forecasting, index
The Home Purchase Sentiment Index

I. Introduction

Housing has long been important in the personal lives and finances of individuals. The 

Great Recession, and the sluggish recovery of the U.S. economy afterward, reinforced judgments 

that housing importantly affects the job market, the financial sector, and the rest of the national 

economy.

An improved understanding of housing can help improve housing and macroeconomic 

policies and forecasts. In 2010, Fannie Mae started its National Housing SurveyTM (NHS) to 

produce new information about consumers’ housing-related attitudes, intentions, and financial 

conditions. The NHS is the only large-scale, national, monthly survey of consumers focused 

exclusively on housing. The responses of the nationally representative sample of 1000 

consumers each month to about 100 survey questions promptly provide information on a wide 

range of housing-related topics.

To effectively and efficiently distill information about consumers’ housing-related 

attitudes, intentions, and conditions, we built the Home Purchase Sentiment Index (HPSI) from 

the responses to six NHS questions. We intend for the HPSI to reflect current housing market

conditions and to provide signals about future conditions in housing markets. In that regard, the 

HPSI is similar in spirit and construction to the University of Michigan’s Index of Consumer 

Sentiment and the Conference Board’s Consumer Confidence Index.

Below we note some of the advantages of survey data generally. We briefly discuss some 

existing consumer and housing indices. We then discuss how we selected component questions 

from the NHS and show the new Home Purchase Sentiment Index (HPSI). We provide evidence 

about its 2012-2015 performance in forecasting important housing market outcomes: prices, 

sales, starts, and mortgage originations. In addition, we show the HPSI by age and by income

and note other groups for which HPSIs can be easily calculated. Finally, we suggest some of the 

other aspects of housing where tailored, survey-based indicators might be built to help analysis 

and forecasting.1

II. Expectations, Surveys, Sentiments, and Housing

Expectations and surveys

Households’ expectations have become central to economic analysis, modeling, and 

policymaking. Long recognized in principle, expectations now figure prominently in practice, 

permeating discussions and forecasts of consumer spending, business investment, labor costs, 

inflation, bond yields, and monetary policy. Incorporating expectations is problematic, however,

 

1 Cai, Deggendorf, and Wilcox [2015] provide more details, explanation, and evidence about the National 

Housing Survey, sentiment indices, and the HPSI.
The Home Purchase Sentiment Index

in large part because most data do not directly measure expectations about the future but, instead, 

measure “the way we were.”

One long established way to get data about expectations is through surveys. Both the 

demand for and supply of surveys, of business analysts, forecasters, consumers, and businesses, 

rose noticeably over the past decade. Because studies increasingly concluded that survey data 

can be informative, and because the costs of conducting and processing surveys decreased, more 

surveys emerged. That so many private-sector organizations produce and pay for access to 

survey data for consumer and business attitudes and conditions testifies to the value of such data.

One practical advantage of survey data for analysis and forecasting is that they are often 

produced quickly, perhaps a few days after the end of a month or even during the month. Second, 

survey data can be especially valuable in the wake of unexpected or especially large 

developments. For example, consumers can quickly change their views, as well as their spending 

and borrowing, in response to a surprising vote in Congress or to an event in the Middle East.

In addition, survey data can be particularly valuable when the news changes consumers’ 

views about longer-run outcomes. Views about longer-run outcomes are particularly important 

for housing and mortgage markets, where longer-run assets and liabilities predominate. And, by 

their very nature, longer-run outcomes don’t produce much traditional data in the shorter-run.

Consumer sentiment indices

Surveys are used in dozens of countries to build indices of overall consumer sentiment. 

Two long-running, well-regarded indices of overall consumer sentiment in the United States are 

the University of Michigan’s Survey of Consumer’s Index of Consumer Sentiment (ICS) and the 

Conference Board Consumer Confidence Index (CCI).2 For similar purposes, businesses are also 

surveyed in many countries. The Michigan Survey uses phone interviews to get 500 households’

views about their personal finances, overall business conditions, and buying conditions. For 

several decades, the ICS has been calculated from the same five questions. In addition to 

questions about recent and expected conditions, both personal and national, one of the five 

questions selected for the ICS asks whether it is a good time to buy big-ticket items.

To produce its monthly CCI, the Conference Board uses the (approximately) 3,500 

responses returned to them from the 5,000 questionnaires they send out by U.S. mail. Like the 

ICS, the CCI is calculated from five questions. The questions selected for the CCI ask consumers 

about current and expected business and job market conditions, and about their expected future 

incomes.

Although their questions and calculation methods differ somewhat, in effect, both the ICS 

and the CCI are based on averages across their five questions of the “net percent positive”

responses in their surveys. After the number of negative (or worse or pessimistic) responses is

subtracted from the number of positive responses for each question, that difference is expressed 

as a percent of aggregate responses.

 

2 For simplicity, we refer to both as indices of overall consumer sentiment.
The Home Purchase Sentiment Index

The newer, higher-frequency Bloomberg U.S. Weekly Consumer Comfort Index is based 

on 1,000 phone interviews to collect consumers’ views on the national economy, the buying 

climate, and their personal financial conditions. In effect, the Consumer Comfort Index is an 

equally-weighted average of the net percent positives for its three categories of questions. While 

the ICS equally weights its questions, the weights applied to the questions in the CCI vary over 

time.

Indices of housing market sentiment or conditions

Some housing-related indices are built from surveys, while others rely on more 

traditional data. The monthly Housing Market Index (HMI) of the National Association of Home 

Builders (NAHB) and Wells Fargo is based on mail surveys of NAHB members. The surveys 

ask builders for their attitudes and expectations for the demand for single-family (SF) homes,

and ask them to rate housing market conditions. Essentially, the HMI is a weighted average of 

the net percent positives for three questions about conditions in and expectations for their local 

SF markets, with weights apparently determined by each question’s historical correlation with 

future SF housing starts.

The National Association of Realtors (NAR) surveys its members monthly about real 

estate market conditions and expectations. Rather than forming an index by combining responses 

to several questions, its REALTORS® Confidence Index (RCI) is a collection of indicators. 

Each indicator is the average of the members’ responses to one of the survey questions, each of 

which is scored as 0, 50, or 100.

Freddie Mac introduced its Multi-Indicator Market Index (MiMi) in March 2014. 

According to the Freddie Mac website, “MiMi measures local housing market conditions by 

combining recent, local-market data with Freddie Mac data...MiMi assesses where each market 

is relative to its own, long-term, stable range....” Rather than being based on survey data, MiMi 

is based on an average of four objective variables.

Consumer sentiment about housing

The monthly NHS started in June 2010. The NHS is the only large-scale, monthly survey 

of consumers that focuses exclusively on housing. The NHS uses phone interviews of 1000 

consumers to get their housing-related attitudes, expectations, and intentions.

3 The NHS is 

designed to produce a sample of respondents that is nationally representative along eight socioeconomic dimensions. To reduce remaining deviations of the sample from national 

 

3 The NHS has adjusted as consumers’ modes of communications have evolved. Starting in October 2014, 

the NHS raised the cell-phone share of its phone calls from 25 to 60 percent. Some, but not all, of those 

respondents also had landline phones. The NHS respondents may then be more representative than those 

in landline-only surveys.
The Home Purchase Sentiment Index

representativeness, each respondent’s answers are weighted before net percent positives are 

calculated. 

The NHS asks consumers for their housing-related views about the recent past, the 

present, the future, and even about hypothetical situations. The NHS asks consumers about their 

personal economic and financial conditions, as well as about the economy as a whole. The NHS 

asks both homeowners and renters about owning and renting homes, home and rental prices, 

home ownership distress, household finances, and views about the condition and outlook for the 

economy.

Thus, the NHS provides valuable information about housing markets that is otherwise not 

available.

4 The resulting, wide-ranging, up-to-date database of consumers’ attitudes, conditions, 

and expectations can provide information quickly and easily, not just to housing analysts and 

economic forecasters, but to borrowers and lenders, to home sellers and home buyers, and to 

investors and policymakers.

III. Evidence of Effects of Consumer Sentiment

Over the past two decades, indices of overall consumer sentiment were integral to a 

number of research studies. Primarily because the ICS and the CCI were the only survey-based 

national indices with sufficiently long time series to be useful, studies of U.S. data typically 

relied either on one or the other.

Here we note but a few of the many studies that concluded that consumer sentiment 

indices added statistically-significantly and often-meaningfully to explanations and forecasts of 

economically and financially important outcomes. Some studies analyzed whether, and to what 

extent, adding a measure of consumer sentiment would have improved forecasts of 

macroeconomic variables, such as GDP, consumer spending, or the probabilities and timing of 

business cycle peaks and troughs [Carroll, et al. 1994; Bram and Ludvigson 1998; Wilcox 2007; 

Levanon 2011]. Other outcomes that consumer sentiment indices (along with variables) have 

been found to help in explaining or forecasting include stock prices, bond yields, corporate credit 

risk, and revenues of particular industries [Matsusaka and Sbordone 1995; Batchelor and Dua 

1998; Longstaff 2002; Lemmon and Portniaguina 2006; Ho and Hung 2009; Barsky and Sims 

2012]. While many studies used data only from the U.S., some also included data from other 

countries, and some did not include U.S. data [Golinelli and Parigi 2004; Dees and Soares Brinca 

2013; Nguyen and Claus 2013]. Taken together, these studies indicate that indices of consumer 

sentiment (or confidence) have long been useful for forecasting or explaining several 

macroeconomic and other important outcomes.

 

4 Some consumer surveys ask a few housing-related questions.
The Home Purchase Sentiment Index

IV. Building the Home Purchase Sentiment Index 

Goals for the HPSI

Our primary goal was to build a single, monthly indicator that would reflect the attitudes, 

conditions, and expectations that importantly influence consumers’ home buying decisions. We 

also sought an indicator that would supplement insights gleaned from other quantitative and 

qualitative analyses about (single-family) housing markets.

By simply combining a small number of NHS questions, we sought an index that would 

provide indications about both current and future conditions in housing markets. Consonant with 

sentiment indices built from the University of Michigan and Conference Board surveys of 

consumers, we refer to our indicator as the Home Purchase Sentiment Index (HPSI). As detailed 

below, the method of calculating the HPSI is simple and is quite similar to the methods used for 

the ICS and the CCI. 

Outcome measures and forecast intervals

As important measures of the strength of single-family housing markets, we chose four 

outcomes: house prices, home sales, housing starts, and mortgage originations. For the level of 

house prices, we used the FHFA purchase-only house price index.5 Home sales data were the 

sum of new, single-family home sales and of existing, single-family home sales.6 Originations 

were the estimated dollar amounts of purchase-money-mortgage originations.7 Housing starts 

were for single-family (1-4) units.8 Each of these series was seasonally adjusted.

When we discuss and present future outcomes, we are referring to their averages during 

entire, upcoming, 12-month intervals. (We occasionally refer to shorter, 6-month future 

intervals.) For example, as of June 2012, future home sales were the total of home sales during 

the 12 months from July 2012 through June 2013. As of June 2012, future house price growth 

was calculated as the change, in percent, of a house price index averaged over July 2011-June 

2012 to its average over July 2012-June 2013.

Preliminary candidate questions for the HPSI 

We started with a long list of candidate questions to include in the HPSI. We chose 28 

questions as preliminary candidates on the basis of data availability, of priori judgments, of 

coverage of important aspects of housing markets, and because of their simple correlations with 

the four housing market outcome measures during 2010-2014. We tilted toward including, but 

did not require, questions that were in the initial (June 2010) NHS. Then, before further 

 

5 Source: U.S. Federal Housing Finance Agency (FHFA).

6 Source: U.S. Bureau of the Census and National Association of Realtors (NAR).

7 Source: Mortgage Bankers Association (MBA). Quarterly data were assigned to mid-quarter months. 

Amounts for other months were derived by linearly interpolating between mid-quarter months. We 

seasonally adjusted the monthly estimates by applying the seasonal factors for home sales.

8 Source: U.S. Bureau of the Census.
The Home Purchase Sentiment Index

analyzing them statistically, we judged which NHS questions were likely to have empirically

meaningful and plausibly reliable connections to housing market outcomes. 

The 28 preliminary candidates covered a wide range of topics that might provide signals 

about housing markets. Some questions asked about personal, and some asked about national

economic and financial conditions, and expectations. One asked whether it is a good time to buy 

a home; another asked whether it is a good time to sell. Questions asked about the difficulty of 

getting a mortgage or about making mortgage payments. Some asked the reasons for, and 

intentions about, buying homes or renting. Questions asked about recent income changes and 

about expected future house prices and mortgage rates.

Paring the list of candidate questions

Although the ICS and the CCI are both calculated from five questions, and in practice

many consumer and housing market indices are built from five questions, there is no analytical 

or practical imperative that dictates using exactly five. Nonetheless, in light of common practice 

and the relatively short history of the NHS, we were inclined to select about five questions for 

the HPSI.

To begin paring down our initial, long list to a “short list” of about 10 candidate 

questions, we used (backward) stepwise regressions of each of the four important housing market 

outcomes on its own lagged dependent variable plus the long list of candidates. We then iterated 

informally, using judgment and stepwise regression results, to select variables for inclusion and 

for exclusion. We generally removed questions that we deemed to have stepwise regression

coefficients of the “wrong” sign. We iterated until our procedure settled on questions that were 

statistically significant at the five percent level or better.9

V. Current Responses to Survey Questions and Future Housing Outcomes

Table 1 lists the topics of the nine questions that remained at the end of our iterative 

procedure. (Also included in Table 1 is question Q12, which asks whether it is a good or bad 

time to buy a house. Q12 is discussed more below.)

<<Table 1 here>>

Table 2 displays the results of our iterative regression procedure. With data available 

through the end of 2014, the estimation period for 12-month-ahead outcomes ended in December 

2013. Each column in Table 2 is a forecasting equation in that it resulted from regressing 

outcomes during the upcoming 12 (or 6) months on the current month value of the NHS 

questions. For both the 12-month-ahead and the 6-month-ahead horizons, each of the four 

 

9 Throughout, we refer to having at least 95 percent confidence, or equivalently having a significance 

level of 0.05 or better (i.e., five percent or lower), that a coefficient or relation differs from zero as being 

“statistically significant,” taking into account the effects of any other variables that were included in a 

regression.
The Home Purchase Sentiment Index

housing market outcomes was statistically significantly affected by at least four of the remaining 

nine candidate questions. Table 2 also shows that Q18 and Q20B, which asked about future rents 

and mortgage rates, significantly improved forecasts for all eight of the outcomes.

<<Table 2 here>>

Q22 and Q109 asked about perceived problems, ex ante and ex post, with mortgage 

credit; Q22 asked whether mortgages are difficult to get. Given the enormous amount of 

discussion about tight mortgage credit over 2010-2014, we expected that Q22 might be 

particularly informative about future housing outcomes. It was not. 

Table 1 also contains Q12 and Q112B. Q12, which asked consumers whether it was a 

good (or bad) time to buy a house, never significantly affected any of the eight outcomes in 

Table 2. Because Q112B, which asked consumers how concerned they were about losing their 

jobs during the next 12 months, did not enter the survey until March 2011, we did not include it 

in our iterative procedure.

House price growth

House price growth proved to be only weakly connected over these years to whether

consumers expected home prices to rise (Q15), and to how much they expected home prices to 

change (Q16&Q17).10 Only for the six-month horizon and then only with small effect (0.01) 

were future home prices correlated with consumers’ expected percentages of house price change 

(Q16&Q17). In contrast to the weak connections of future to expected house price growth, house 

prices were forecasted to grow significantly faster when more consumers’ expected mortgage 

rates to decline (Q20B). 

Forecasts of house price growth were also significantly higher when more consumers 

reported that mortgages were easy to get (Q22), fewer were stressed about debt payments 

(Q109), more reported recent income gains (Q116), and more expected rents to rise (Q18).

Neither consumers’ responses about whether it was a good (or bad) time to buy a house (Q12, 

which is not shown) nor whether it was a good (or bad) time to sell a house (Q13) was 

significantly connected to future house price growth, once the effects of the other included 

variables were taken into account. 

Home sales

Columns 3 and 4 show that future home sales were significantly connected to responses 

about a good time to sell and whether house prices, rents, and mortgage rates were expected to 

rise, as well as to stress about debt payment.

 

10 Questions Q16 and Q17 separately asked by what percent consumers expected house prices to go up 

and go down over the next 12 months.
The Home Purchase Sentiment Index

Housing starts

When more consumers responded that it was a good time to sell homes, then housing 

starts tended to be stronger over the following year. Expectations of higher rents and lower 

mortgage interest rates, as well as more responses that mortgages were easy to get, also tended to 

foreshadow more housing starts in the future.

Mortgage originations

More (dollars of new, purchase-money) originations in the future were also forecasted by 

more responses that it was a good time to buy a house, that house prices and rents would rise, 

and that mortgage rates would fall over the next 12 months. Future originations were also higher 

when fewer consumers reported that they were stressed by their debt payments. 

VI. Building the Home Purchase Sentiment Index

Criteria for HPSI questions

From the ten questions in Table 1, on the basis of judgment and statistical evidence, we 

ultimately selected the six questions in Table 3 for the HPSI. We sought to include questions that 

would reflect the factors presumed to drive home purchases: incomes, interest rates, and house 

prices – whether experienced or expected. We also wanted the HPSI to reflect households’ 

uncertainties about those factors because home ownership entails large, long-term financial and 

social commitments. We sought questions that were likely to reflect perennial, rather than 

ephemeral, considerations. We sought questions that seemed more likely to bear steady, rather 

than fluctuating relations to housing outcomes. We sought questions that had some track record 

in forecasting, regardless of whether they caused future housing market outcomes. In choosing 

the six component questions, we used statistical evidence like that in Table 2. We also used

statistical evidence for a longer time period that was based on Michigan survey data.

11 And, 

ultimately, we used judgment to fill in for the absence of statistical evidence and sometimes to 

overrule statistical evidence.

<<Table 3 here>>

The six component questions differ in how well they satisfy our criteria. In addition to 

their logical appeal, questions Q13, Q15, Q20B, and Q116 had considerable statistical support in

Table 2. We thought that responses to Q2, whether it is a good (or bad) time to buy a house, 

were likely to give quantitatively important and reliable signals about future housing market 

outcomes. While the a priori case for including Q12 seemed strong, the statistical evidence from 

the 2010-2013 period was weak. On the other hand, the statistical evidence based on Michigan 

survey data for a longer period strongly favored including Q12, regarding a good time to buy

 
The Home Purchase Sentiment Index

[Wilcox 2015]. Over longer or less exceptional periods, Q12 might well be more informative.

Indicators of employment conditions often appear in other consumer indices. Two CCI questions 

focus on the job market. Nonetheless, because we judged that job-loss concerns would be 

quantitatively important and fairly reliable signals for understanding and forecasting housing 

market outcomes, we chose to include Q112B in the HPSI.

HPSI’s six component questions: 2011-2014

Figure 1 shows the HPSI (thick black line) and the net percent positive responses to its 

six component questions for March 2011-December 2014. The six questions that we used to 

calculate the HPSI are: “good time to buy a house” (Q12 (black, solid line)), “good time to sell a 

house” (Q13 (gray, solid line)), “expectations of higher home prices” (Q15 (black, dashed line)),

“expectations of lower mortgage rates” (Q20B (gray, dashed line)), “household income 

increased” (Q116 (black, dotted line)), and “not concerned about job loss” (Q112B (gray, dotted 

line)). Because the NHS began asking consumers about their job loss concerns (Q112B) in 

March 2011, the data in Figure 1 also begin then. Table 2 shows that housing markets tended to 

strengthen after declines in the net percent positive responses to two of the HPSI component 

questions: “expectations of higher mortgage rates” and “job loss concerns.” So that plots of their 

data in Figure 1 rose when these two questions signaled stronger housing market conditions, 

Figure 1 plots the negative of the net positive responses. Figure 1 also reminds us the net percent 

positive responses can be either positive or negative. 

<<Figure 1 here>>

In contrast to the responses about whether it was a good or bad time to sell a house, the 

net percent positive of those who responded that it was a good time to buy a house, Q12, was 

strongly positive throughout this period. As we had expected, Q12 rose and fell as the housing 

market and the outlook for housing rose in 2011-2012 and fell in 2013. But, in light of the 

housing turmoil then, its volatility was surprisingly low.

The path of Q13, whether it was a good time to sell a house, was strikingly different than 

that of Q12, whether it was a good time to buy a house. First, the net percent positive was always 

negative: There were always fewer respondents who said it was a good time to sell than said that 

it was a bad time to sell a house. Second, “good time to sell a house” rose sharply (from nearly -

80) through Spring 2013 and then plateaued for about a year before it generally trended upward 

through the end of 2014.

The net percent positive responses to whether home prices will rise over the upcoming 12 

months, Q15, hovered around 10 percent during 2011, rose steadily through the middle in the 

middle of 2013, and remained at nearly 40 percent at the end of 2014.

The net percent positive responses to whether mortgage rates would fall, Q20B (gray, 

dashed line), ranged from minus 20 to minus 40 percent. Thus, on balance, consumers responded 

that they expected mortgage interest rates to rise. The decline in rates after the middle of 2012 
The Home Purchase Sentiment Index

coincided with more responses that rates would decline in the future; the rise in rates that started 

in the Spring of 2013 (the “taper tantrum”) coincided with more consumers expecting that 

mortgage rates would rise after that. It is striking how often and how large the net percent of 

respondents was that expected higher mortgage rates. But, for forecasting purposes, the time path 

of Q20B still seems likely to be informative. And, as Table 2 shows, expectations about 

mortgage rates very reliably forecasted housing markets. Indeed, Q20B was the only HPSI 

component that significantly improved forecasts for all eight housing market outcomes in Table 

2.

The net percent of respondents whose incomes increased over the past year, Q116, was 

always positive and tended to rise slowly over this entire period. If the economy were booming, 

labor markets were tight, and inflation closer to its historical averages, we might expect to see 

high and volatile values for Q116. We did not. The slowly declining concerns about job loss 

comport with the low and slowly rising net percent of respondents with higher incomes.

Calculating the Home Purchase Sentiment Index

Given its six component questions, how did we calculate the Home Purchase Sentiment 

Index (HPSI)? Simply: The HPSI is the average of the net percent positive responses of each of 

its six component questions.

12 The only other step in calculating the HPSI, which we included

for technical and presentation reasons, is to add 63.5 to the average of the net percent positive 

responses. (Adding 63.5 makes the initial (March 2011) value of the HPSI equal 60.)

The HPSI is an “equal weighted” index in that we take a simple, rather than a weighted,

average, of the net percent positives. Using equal, as opposed to data based or otherwise chosen

weights has a number of advantages. First, equal weights make the HPSI fast, easy, and accurate 

to calculate each month. As soon as the NHS data are available, HPSI can be calculated without 

analysis, decisions, or adjustments. Second, equal weights are much easier to explain and 

comprehend. Third, it is common, though not universal, for consumer indices to have equal 

weights; for example, the ICS is based on equal weights.

A case can be made that unequal and time varying weights would be preferable. Many 

quantity and price indices have weights that change frequently. If weights were based on data, 

perhaps in the form of regression results, then incoming data would seem to argue for time 

varying weights. One drawback of time varying weights is they suggest revising past weights 

and, thus, index values. We also eschewed seasonal adjustments to the HPSI and to its 

component questions.

Nonetheless, the components contribute very unequally to the equally weighted HPSI. 

Relative contributions can be measured by relative volatility. In Figure 1, the HPSI mostly 

moved due to its more volatile components during 2011-2014: good time to sell, home prices 

will go up, mortgage rates will go up.

 

12 As noted earlier, for the two questions whose positive responses were negatively correlated with 

housing market outcomes, we use their net percent negative responses.
The Home Purchase Sentiment Index

HPSI history: 2011-2014

The Home Purchase Sentiment Index declined from its beginning in March 2011 through 

the summer of 2011, when there was great uncertainty about the resolution and effects of the 

federal debt ceiling difficulties. From then until about the middle of 2013, the HPSI rose quite a 

lot and quite steadily. As Figure 1 shows, each of the HPSI components contributed to its rise, 

except for the question about whether mortgage interest rates were expected to go down. In the 

spring of 2013, the HPSI spiked up, primarily due to the uptick in “good time to sell.” The HPSI 

then declined considerably through the second half of 2013. The surge in mortgage rates in the 

middle of 2013 apparently triggered downdrafts, not only in “mortgage rates will go down,” but 

also, noticeably and not surprisingly, in “good time to buy” and in “home prices will go up.”

During 2014, the HPSI went up and down and back up, with a downtick in the fall of 2014. 

VII. Wider Uses for Narrower HPSIs

HPSI indices can be calculated easily for specific groups or conditions. To illustrate, 

Figures 2 and 3 plot the HPSI for selected income and age groups. Figure 2 shows that the HPSI 

rose considerably more for the highest-income group (who had household incomes of more than 

$100,000) than it did for the middle or lowest income group in Figure 2. Figure 3 shows that, 

compared with the overall HPSI, during 2011-2014 the HPSI rose less for the youngest (18-34 

years) group and perhaps more than the overall HPSI among the group aged 35-44 years. 

<<Figure 2 here>>

<<Figure 3 here>>

One benefit of the simple HPSI calculation method is that it can be applied so easily to 

obtain targeted indices, such as HPSIs for married people living in the Northeast, for younger 

people with higher incomes, or for younger renters with recent increases in incomes. Of course, 

finer divisions of the total national monthly sample size of 1000 respondents leave fewer 

consumers per group. Nonetheless, the information about a target group may often outweigh the 

additional sampling errors.

The more disparate the attitudes, intentions, and conditions of a larger group, the more 

likely that HPSIs for narrower target groups will be valuable. Regional and demographic factors 

are likely to contribute more to housing markets in most future periods than they did over the 

past decade of national housing market boom, bust, triage, and recuperation. If they do, then 

differences in HPSI movements across regions and groups are also likely to be larger than we 

have observed so far. Then, HPSIs by region and by group may be especially informative and 

valuable.
The Home Purchase Sentiment Index

VIII. HPSI Performance: Forecasting Housing Outcomes

The HPSI may provide valuable signals about future housing market outcomes, either 

when used alone or when used to supplement other information, or both. The figures and tables

that follow show that, to varying degrees, movements of the HPSI during 2011-2013 did signal 

future house price growth, home sales, housing starts, and mortgage originations. Evaluation of 

its forecasting performance during this period, of course, should take into account that memories 

of this period are still relatively fresh and that we pared down the number of candidate questions 

and then finalized the six HPSI questions partly on the basis of Table 2’s forecasting 

performances of individual NHS questions.

House price growth

Figure 4 plots the level of the HPSI (solid black line) and the growth rates of house prices 

over the next 12- (dashed black line) and 6-month (solid gray line) intervals for March 2011-

June 2014. It is apparent that movements of the HPSI did not forecast well the rise or the fall of 

house price growth during 2012-2015. While the HPSI steadily marched up, the growth rate of 

house prices over future 12-month intervals rose from 2 to 8 percent and then settled in the 

neighborhood of 5 percent. The correlations of the HPSI with the future growth rates of house 

prices over 6-month and 12-month horizons were 0.17 and 0.33. Column 1 of Table 4 shows 

that, during 2011-2013, future house price growth for 12-month horizons was not significantly 

related to the current HPSI.

<<Figure 4 here>>

Other forecasts of house price growth fared little, if any, better than the HPSI. Responses 

to NHS and Michigan survey questions that were asked directly show that households’ 

expectations fell short of actual future house price growth rates by amounts that were virtually 

always positive, were often large, and were variable. Professional forecasts also tended to underestimate high and rising house price growth rates in 2012 and 2013.

Home sales

Figure 5 substitutes home sales for the house price growth rates that appeared in Figure 4. 

The milder slowdown in home sales than in house price growth rates meant that the upward

trending HPSI (solid black line) was correlated more with home sales than with house price 

growth rates. The results in column 2 of Table 4 support that that perspective, since the HPSI 

coefficient was significant at more than 99 percent and the regression’s R2jumped to over 70 

percent. As Figure 5 shows, the HPSI was even more correlated with home sales six months 

ahead (solid gray line). Naturally enough, the NHS respondents had not foreseen, at either 12-

month or six-month horizons, the “taper tantrum” or the abrupt increases in mortgage rates in the 

spring of 2013, with their ensuing tolls on home sales. Nor is it likely that consumers foresaw the 
The Home Purchase Sentiment Index

declines in mortgage rates during 2014, declines apparently triggered in large part by 

international political events and by news from global oil markets.

<<Figure 5 here>>

Housing starts and mortgage originations

Column 3 of Table 4 shows that the HPSI was even more highly correlated with future 

single-family housing starts than with sales or prices. Column 4 then shows that future 

(purchase-money) mortgage originations were highly correlated with current values of the 

HPSI.13

<<Table 4 here>>

Thus, the current HPSI was significantly related to home sales, single-family starts, and 

mortgage originations. Notable, however, was the much weaker relation over this period of the 

HPSI to future house price growth rates.

IX. Forecasting Horse Races

HPSI vs. MHPSI

Here we compare the forecasting ability of the HPSI to the abilities of two indices that 

are based on the University of Michigan’s Survey of Consumers: the Index of Consumer 

Sentiment (ICS) and a housing-oriented index (MHPSI). We calculated the MHPSI as the 

average of the net percent positive responses to five questions in the Michigan survey that are 

similar to those we used to build the HPSI. Not surprisingly, the MHPSI was highly correlated 

(0.93) over March 2011-December 2014. Both series trended upward then, sagged noticeably in 

the middle of 2013 following the bond market’s taper tantrum, and then in early 2014 generally 

resumed their upward marches.

A natural question is whether the HPSI adds information to that in the data for the long

running ICS or data for the MHPSI. A statistical “horse race” is a time-honored way to help 

answer such a question. Table 5 has race results. Columns 1, 3, and 5 show regressions for the 

estimation period of March 2011-December 2013 of future housing market outcomes on the 

HPSI and the MHPSI simultaneously.

<<Table 5 here>>

 

13 We seasonally adjusted the MBA’s originations data with a seasonal adjustment routine in STATA. We 

had to use a different method to seasonally adjust the short span of Fannie Mae data.
The Home Purchase Sentiment Index

In its head-to-head race against MHPSI, the HPSI clearly won. Rows 1 and 2 in Table 5 

show that the three future housing outcomes (home sales, single-family starts, and mortgage 

originations) were each significantly related to the HPSI, but not to the MHPSI.14 Thus, these 

race results imply that including HPSI improved forecasts of housing market outcomes, while the 

MHPSI did not. The source of HPSI’s stronger performance is not completely clear. One 

possibility is that it stems from differences in questions: The NHS asks specifically about 

mortgage rates, while the Michigan survey asks about borrowing rates in general. If, for 

example, the Fed’s QE programs lowered mortgage interest rates and lowered their correlation 

with consumers’ borrowing rates generally during this period, that ought to raise the HPSI’s 

forecasting performance relative to that of the MHPSI.

Housing Sentiment vs. Consumer Sentiment

Another question is whether the HPSI improves forecasts once we take into account 

overall consumer sentiment. Given its focus on housing, we would surely expect the HPSI to 

perform well in housing forecast horse races against the ICS. Table 5 shows that it does: For 

each of the three housing outcomes, the HPSI was strongly significant, while the ICS did not 

detectably improve housing forecasts. The higher R-squared statistics in columns 1, 3, and 5 than 

in columns 2, 4, and 6 show that, of the two Michigan-based indices, the MHPSI added more to 

housing forecasts than the ICS did. In that regard, the results in columns 2, 4, and 6 in Table 5 

reinforce the usefulness of a housing-focused sentiment index for forecasting housing markets.

Since 2000, national factors may have had unusually large effects on housing. At the 

same time, national factors apparently caused the housing sector to diverge more than usual from 

the national economy. Times when housing does diverge more are the times when housingfocused information, such as that in the HPSI, is likely to be more valuable. And, in turn, indices 

of overall consumer sentiment like the ICS seem likely to forecast overall consumer spending 

better at those times than either the MHPSI or the HPSI would. Thus, housing-focused and 

overall consumer sentiment are both likely to be especially valuable at the same time.

Conclusions and Extensions

We used data from Fannie Mae’s National Housing Survey to build the Home Purchase 

Sentiment Index (HPSI). Using six survey questions, we built the HPSI to indicate consumers’ 

sentiments toward housing and to provide signals about current and, especially, about future 

housing market conditions. Since the HPSI started in March 2011, its increases were quite 

reliably followed by stronger housing markets. In forecasting horse races, the HPSI 

outperformed the Index of Consumer Sentiment and a housing-oriented index that we built with 

data from the Michigan survey.

 

14 Neither index, alone or simultaneously, was significantly related to future house prices.
The Home Purchase Sentiment Index

The simplicity of the HPSI makes it easy to calculate HPSIs for groups of particular 

interest. As examples, we showed HPSIs by income and by age. HPSIs can be calculated easily

for many other target groups.

In addition, NHS data can be used to build indices to signal developments other than 

home purchase sentiment. Examples might include specialized indices that aim to measure 

millennials’ or seniors’ housing attitudes and conditions, to help forecast renters’ attitudes, to 

predict home owners’ moving, or to understand or forecast mortgage refinancing, delinquency, 

and default.
The Home Purchase Sentiment Index

References

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Innovations in Consumer Confidence." American Economic Review, 102(4) June: 1343-77.

Batchelor, Roy, and Pami Dua. 1998. “Improving Macro-Economic Forecasts: The Role of 

Consumer Confidence.” International Journal of Forecasting. 14(1): 71-81.

Bram, Jason and Sydney Ludvigson. 1998. “Does Consumer Confidence Forecast Household 

Expenditure? A Sentiment Index Horse Race.” FRBNY Economic Policy Review. 4(2) June: 

59-78.

Cai, Qiang, Steve Deggendorf, and James A. Wilcox. 2015. “Building a Home Purchase 

Sentiment Index.” working paper. September. 

http://www.fanniemae.com/resources/file/research/housingsurvey/pdf/hpsi-whitepaper.pdf

Carroll, Christopher D., Jeffrey Fuhrer, and David W. Wilcox. 1994. “Does Consumer Sentiment 

Forecast Household Spending? If So, Why?” American Economic Review. 84(5) December: 

1397-1408.

Dees, Stephane, and Pedro Soares Brinca. 2013. “Consumer confidence as a predictor of 

consumption spending: Evidence for the United States and the Euro area.” International 

Economics. 134 December: 1-14. http://dx.doi.org/10.1016/j.inteco.2013.05.001.

Golinelli, Roberto and Giuseppe Parigi. 2004. “Consumer Sentiment and Economic Activity: A 

Cross Country Comparison.” Journal of Business Cycle Measurement and Analysis. 1(2): 147-

170.

Ho, Chienwei and Chi-Hsiou Hung. 2009. “Investor Sentiment as Conditioning Information in 

Asset Pricing.” Journal of Banking & Finance. 33(5) May: 892-903.

Lemmon, Michael and Evgenia Portniaguina. 2006. “Consumer Confidence and Asset Prices: 

Some Empirical Evidence.” Review of Financial Studies. 19(4): 1499-1529.

Levanon, Gad. 2011. “Forecasting Recession and Slow-Down Probabilities with Markov 

Switching Probabilities as Right-Hand-Side Variables.” Business Economics. 46(2) April: 99-

110.

Longstaff, Francis A. 2002. “The Flight-to-Liquidity Premium in U.S. Treasury Bond Prices.” 

National Bureau of Economic Research Working Paper 9312. October.

Matsusaka, John G., and Argia M. Sbordone. 1995. “Consumer Confidence and Economic 

Fluctuations.” Economic Inquiry. 33(2) April: 296-318.
The Home Purchase Sentiment Index

Nguyen, Viet Hoang, and Edda Claus. 2013. “Good news, bad news, consumer sentiment and 

consumption behavior.” Journal of Economic Psychology. 39(C) December: 426-438. 

http://dx.doi.org/10.1016/j.joep.2013.10.001.

Wilcox, James A. 2007. “Forecasting Components of Consumption with Components of 

Consumer Sentiment.” Business Economics. 42(4) October: 22-32.

____________. 2015. “Forecasting Housing with Household Sentiment.” In process. July.
The Home Purchase Sentiment Index

Table 1 

National Housing Survey Question Numbers and Topics

Question Topic

1. Q12 Good time to buy a house

2. Q13 Good time to sell a house

3. Q15 or

Q16&Q17 Expectations of higher home prices

4. Q18 Expectations of higher rents

5. Q20B Expectations of higher mortgage rates

6. Q22 Difficult to get a mortgage

7. Q109 Stressed about debt payments

8. Q112B Concerns about job loss

9. Q116 Household income increased

10. Q117 Household expenses increased
The Home Purchase Sentiment Index

Table 2

Regressions of Future Housing Outcomes on Current Individual NHS Questions

Independent 

Variables

Dependent Variables: Housing Outcomes During the Next 12 (or Six) Months

House Price Growth Home Sales Single-Family Starts Mortgage Originations

12 months 6 months 12 months 6 months 12 months 6 months 12 months 6 months

(1) (2) (3) (4) (5) (6) (7) (8)

1. Q13 573** 548*** 242*** 199*** 147*** 139**

2. Q15 656** 1,256*** 137*** 266*** 301***

3. Q16&Q17 0.01***

4. Q18 0.45*** 0.49*** 3,116*** 2,891*** 612*** 446*** 317*** 269**

5. Q20B -0.25*** -0.33*** -2,166*** -2,161*** -368*** -350*** -410*** -237***

6. Q22 -0.14** -208** -168**

7. Q109 -0.095** -787** -220***

8. Q116 0.17**

9. Q117 0.16**

Observations 43 49 43 49 43 49 43 49

R-squared 0.907 0.864 0.930 0.918 0.937 0.936 0.927 0.852

Notes: Asterisks denote statistical significance at the 10 (*), 5 (**), and 1 (***) percent levels. The table shows the results of the iterative procedure described in 

the text. The estimation period was June 2010 – December 2013 (June 2014) for the 12-month (six-month) forecast horizon, or interval, except for house price 

growth, which we had the 12-months-ahead data through November 2013 and six-months-ahead data through May 2014. Housing market outcomes were 

seasonally adjusted. Data for net percent positive responses to NHS questions were not seasonally adjusted.
The Home Purchase Sentiment Index

Table 3

The Six Component Questions of the Home Purchase Sentiment Index (HPSI)

Question Question Topic Time Period

1. Q12 Good time to buy a house Current period

2. Q13 Good time to sell a house Current period

3. Q15 Expectations of higher home prices Next 12 months

4. Q20B Expectations of higher mortgage rates Next 12 months

5. Q112B Concerns about job loss Next 12 months

6. Q116 Household income increased Past 12 months
The Home Purchase Sentiment Index

Table 4

Regressions of Future Housing Outcomes on the Current HPSI

Independent

Variables

House Price

Growth

Home

Sales

Single-Family

Starts

Mortgage 

Originations

(1) (2) (3) (4)

1. HPSI 0.0608 0.0350*** 0.0082*** 10.58***

(1.39) (8.90) (11.15) (12.21)

2. Constant 1.53 2.78*** 0.008 -96.79

(0.51) (10.16) (0.16) (-1.61)

Observations 34 34 34 34

R-squared 0.057 0.712 0.795 0.823

Notes: t-statistics in parentheses. Asterisks denote statistical significance at the 10 (*), 5 (**), and 1(***) percent 

levels. Future outcomes are calculated over upcoming 12-month intervals, The monthly estimation period was 

March 2011 – December 2013.
The Home Purchase Sentiment Index

Table 5

Forecasting Horse Races: HPSI vs. MHPSI and vs. ICS

Independent

Variables

Home 

Sales

Home 

Sales

Single-Family

Starts

Single-Family

Starts

Mortgage 

Originations

Mortgage 

Originations

(1) (2) (3) (4) (5) (6)

1. HPSI 0.0245** 0.0344*** 0.0064*** 0.0086*** 8.69*** 10.61***

(2.50) (5.033) (3.43) (6.79) (4.00) (7.03)

2. MHPSI 0.0098 --- 0.0018 --- 1.75 ---

(1.18) --- (1.16) --- (0.95) ---

3. ICS --- 0.0007 --- -0.0005 --- -0.02

--- (0.11) --- (-0.44) --- (-0.02)

4. Constant 2.91*** 2.77*** 0.032 0.0147 -73.55 -96.95

(9.89) (9.62) (0.58) (0.26) (-1.13) (-1.53)

Observations 34 34 34 34 34 34

R-squared 0.725 0.713 0.804 09.797 0.828 0.823

Notes: t-statistics in parentheses. Asterisks denote statistical significance at the 10 (*), 5 (**), and 1(***) percent 

levels. Future outcomes are calculated over upcoming 12-month intervals. The estimation period was March 2011-

December 2013.
The Home Purchase Sentiment Index

Figure 1

HPSI and Its Six Component Questions

(Monthly, not seasonally adjusted, March 2011 – December 2014)
The Home Purchase Sentiment Index

Figure 2

HPSI by Household Income Group

(Monthly, not seasonally adjusted, March 2011–December 2014)
The Home Purchase Sentiment Index

Figure 3

HPSI by Age Group

(Monthly, not seasonally adjusted, March 2011–December 2014)

50

60

70

80

90

100

HPSI

Dec2010 Jun2011 Dec2011 Jun2012 Dec2012 Jun2013 Dec2013 Jun2014 Dec2014

HPSI HPSI: Ages 18-34

HPSI: Ages 35-44
The Home Purchase Sentiment Index

Figure 4

HPSI and Future House Price Growth over 12- and Six-Month Horizons

(Monthly, house price growth: percent SAAR, March 2011–June 2014)

30

40

50

60

70

80

90

100HPSI

-4

-2

0

2

4

6

8

10

Percent

Dec2010 Jun2011 Dec2011 Jun2012 Dec2012 Jun2013 Dec2013 Jun2014 Dec2014

Future house price growth next 12 months Future house price growth next 6 months

HPSI
The Home Purchase Sentiment Index

Figure 5

HPSI and Future Home Sales over 12- and Six-Month Horizons

(Monthly, home sales: seasonally adjusted, annual rate, millions, March 2011–June 2014)

30

40

50

60

70

80

90

100HPSI

4.25

4.5

4.75

5

5.25

5.5

5.75

6 Millions

Dec2010 Jun2011 Dec2011 Jun2012 Dec2012 Jun2013 Dec2013 Jun2014 Dec2014

Future home sales next 12 months Future home sales next 6 months

HPSI