Housing Prices and Expectations: A Study of Auckland Housing Market

Housing Prices and Expectations: A Study of Auckland Housing Market

YANG YANG

¹

, MICHAEL REHM

¹

and MINGQUAN ZHOU

²

1Department of Property, the University of Auckland

2Department of Accounting and Finance, the University of Auckland

Abstract: There is a lack of consensus in terms of whether expectations are adaptive or rational. This

study examines whether property buyers’ expectations are adaptive in Auckland, a metropolis that has experienced rampant housing speculation during the past two decades. The paper also studies the interplay between expectations and housing prices. We establish two vector error correction models:

one with survey-based expectations and another one with our modelled adaptive expectations. By comparing the results of Granger causality tests and impulse response analyses, we have discovered that, in the Auckland housing market, expectations form based on historical price fluctuations. In addition, we have demonstrated the evidence of a feedback cycle: realised housing capital gains lift expectations which in turn spur Auckland property price growth.

Key Words: Housing prices; Adaptive expectations; VECM

1. Introduction

A large body of housing literature has agreed that expectations are important determinants of asset prices. When participants believe that market prices will behave in a certain way, their behaviour aligns with the expectations in their minds and the participants’ activities drive the prices towards the expected direction. Wong and Hui (2006, p. 498) describe the mechanism by which expectations affect housing prices by pointing out that “so long as people expect an increase in house sale prices, their behaviour will generate more demand—thereby pushing up prices, wages and production costs, setting up a circle of price-expectation adjustment”.

Although many believe that expectations play a role in the fluctuations of housing prices, opinions differ substantially in terms of whether expectations are adaptive or rational. Those who support adaptive expectation hypothesis hold the view that investors form their expectations in a backward-looking manner—they expect that recent price history will prevail in the future. For instance, Case and Shiller (1988, p.21) claim that “people seem to form their expectations on the basis of past price movements rather than any knowledge of fundamentals”. To test the effects of expectations on housing prices, Brown (1990) establishes both adaptive and rational models and finds that the former demonstrates a better fit. Nordvik (1995) states that, in housing markets, expectations form through extrapolation of past price trend rather than through market fundamentals. Similarly, in share markets, He and Shen (2010) conclude that investors extrapolate from past stock returns and previous earnings growth rates.

Furthermore, from a statistical point of view, Chow (2011) claims that the rational expectations hypothesis is used without sufficient reasons and he provides strong evidence supporting adaptive expectations hypothesis. Recently, Mohan et al. (2019) find that recent movements of housing prices significantly influence expectations of future prices.

On the other hand, researchers who believe in the rational expectation hypothesis argue that economic agents use all information available to predict future price movements. An approach to assess the

existence of bubbles is to calculate the difference between nominal asset prices and rational expectation prices so rational expectation models are widely used in identifying bubbles (Hou, 2010). Muth (1961) is the first one who formulates the hypothesis of rational expectation that becomes popular in the 1980s.

He publishes a seminal paper on modelling expectations and asserts that participants’ expectations rely on the whole economic system with no information being wasted. Lucas (1976), however, criticises the use of adaptive expectations to predict the outcomes of economic policies in what is known as “Lucas Critique”. He suggests that market players are forward-looking and are able to modify expectations based on new policies. Tse (1994) comments that expectations tend to respond to fundamental values instead of historical price fluctuations. Wong et al. (2005) think that expectations are built upon investors’ forward-looking confidence in the economy. Bailey et al. (2018) challenge the adaptive expectations hypothesis as they find that social interactions play an important part in determination of expectations.

The purpose of this paper is twofold. First, it tests whether and to what extent adaptive expectation hypothesis holds true in Auckland, a metropolis that has experienced extensive speculation on housing and a considerable increase in real estate prices over the past two decades. Second, it studies the impacts of expectations on Auckland housing prices and the impacts of Auckland housing price movements on expectations. To be more specific, this paper uses two kinds of expectations: surveyed expectations and modelled adaptive expectations. The relationships between the two forms of expectations and housing prices are examined using Granger causality tests and impulse response analyses.

This paper is arranged as follows. It first reviews the current literature on adaptive expectations and the relationship between expectations and housing prices. It goes on to discuss data and its research design.

The paper then presents and explains findings. The final section concludes the article.

2. Literature Review

Existing literature has found that high expectations are often associated with increases in real estate prices. Expectations are intangible. To study the influence of expectations, a question arises: how to model unobservable expectations? A common and simple way to quantify expectations is to adopt adaptive expectation hypothesis and assume the recent price history continues in the next period. For instance, Phillips (1988), Kim and Suh (1993), Levin and Wright (1997), Ihlanfeldt and Shaughnessy (2004), Malpezzi and Wachter (2005), Cadil (2009) and Sommervoll et al. (2010) model expectations by assuming expected capital gains are determined by price changes in the last period. This method of measuring expectations is easy to implement but simplistic as market players usually consider not only price movements in the most recent period but also long-term price trend. A simple variant of this method is to posit that expectations are the arithmetic mean of price changes over a few periods. For example, Phillips (1988), Diamond (1985) and Taltavull et al. (2012) model expectations by using the average of housing price appreciation in the past three, eight and ten years respectively.

Another approach to gauge expectations also assumes that expectations form based on previous price fluctuations. This method, however, places higher weights on recent price changes and lower weights on distant price history as market participants’ extrapolation tend to focus more on the latest price dynamics. Muth (1961) points out that expected prices can be expressed as geometrically weighted moving averages of past prices. Chow (2011) state that previous price fluctuations with geometrically declining weights can be used as a proxy for future expectations. Boudoukh et al. (1998) use R(t), R(t- 1) ,…, R(t-k+1) to denote the most recent k returns and assign weights [(1-λ)/(1- λ^k)], [(1-λ)/(1- λ^k)]λ ,…, [(1-λ)/(1- λ^k)]λ^k-1, respectively. λ is called as the decay factor. As shown by Figure 1, a λ close to 1 indicates a slow rate of declining weights.

Figure 1. The values of the decay factor and their rates of decline.

Source: authors’ own calculations.

There are other ways of modelling expectations. Kim and Kim (1999) assume that expected future prices are a weighted average of the expectation in the last period and the current asset price. Towbin and Weber (2015) use vacancy rates as a variable to quantify housing price expectations. They believe that “When prices are expected to rise, there is an increase in construction activity and people are reluctant to sell now because they expect higher profits by selling later (Towbin and Weber, 2015, p.9)”.

Therefore, the vacancy rate rises as the expectations increase.

Rational and adaptive expectation hypotheses aside, some studies rely on actual expectations from surveys. Lambertini et al. (2013) use the Michigan Survey of Consumer that asks market players whether it is currently a good time to buy a house. The researchers focus on the proportion of participants who think the timing is good and include the percentage in a vector autoregression (VAR) model. They conclude that shocks to expectations account for a sizable share of the fluctuations in housing prices. Ling et al. (2015) adopt survey-based sentiment and find it is significantly predictive of future price changes in boom and bust phases and “normal” periods. Towbin and Weber (2015) compare modelled price expectations derived from a Bayesian vector autoregressive model with the expectations

generated from the Michigan Survey of Consumer. They conclude that there is a positive correlation between model-based and survey-based expectations. In order to estimate a labour demand function, Ilmakunnas (1989) use both business surveyed expectations and rational expectation hypothesis. He points out that survey data has an advantage over the rational expectation hypothesis because the estimation with survey data is simpler and the model’s parameters are more realistic.

The last group of papers investigate the interplay between expectations and housing prices. Based on the rational expectation hypothesis, Kim and Suh (1993) find that speculation, proxied by expected prices, has caused land prices to deviate from long-run equilibrium. With the assumption of adaptive expectation, Levin and Wright (1997) and Malpezzi and Wachter (2005) discover that elevated expectations add pressure on housing prices. Mallick and Mahalik (2012) employ a VAR model as a framework to test the relationships between housing price growth and its determinants. They claim that speculation, reflected by recent price history, contributes a significant share of increases in housing price. Towbin and Weber (2015) disentangle realistic expectations from unrealistic ones and find that the former and the latter explain about 25% and 9% of housing price increase during the study period.

A possible contribution of the present research is that it marks the first attempt to test the relationships between housing prices and two forms of expectations: survey-based and adaptive. Towbin and Weber (2015) make a comparison of modelled expectations, which is proxied by vacancy rates, and survey expectations. They also examine the dynamics between the two kinds of expectations and housing prices. Apart from the aforementioned study, the vast majority of current research tends to adopt only one expectation indicator when examining its interplay with housing prices. This article, however, is the first study that encompasses survey-based expectations and adaptive expectations to test their relationships with housing prices. By comparing the two forms of expectations, the research aims to reveal whether investors’ expectations are backward-looking in a metropolis that has experienced rampant housing speculation.

3. Data and Method

This study uses quarterly data covering a period from 2003 to 2016. There are six variables: Auckland housing price index, model-based expectations, survey-based expectations, effective mortgage interest rates, the number of building consent issued in Auckland and net permanent and long-term immigrants entered into New Zealand.

Auckland property sale data including all residential transactions from 2002 to 2016 are sourced from Auckland Council. The first variable, Auckland housing price index, is constructed using a hedonic regression based on the gross sale prices of stand-alone house transactions, which account for 77% of total Auckland residential sales¹. The coefficients of the hedonic regression are indexed to a starting point of 100 in the first quarter of 2003, as shown in Figure 2. The price index is developed on a quarterly basis with 2002 being the base year. There are two advantages of the index. First, it is constructed on the basis of property transactional level data. Clapham et al. (1999) and Geltner and Ling (2006) claim that a housing price index built upon disaggregated data is a close reflection of price dynamics. Second, the frequency of the index is quarterly. Shi et al. (2010) argue that using high frequency index increases the degree of freedom in a VAR model and is more likely to reveal accurate relationships among variables. As shown by Figure 2, Auckland freestanding house prices, on average, rise substantially over this timeframe except for the period from 2008 to 2012 as the market reacts to the Global Financial Crisis.

Figure 2. Auckland housing price index (2003-2016)

1 Authors’ own calculations based on the Auckland property sale data.

Note: The coefficients of the hedonic regression are indexed to a starting point of 100 in the first quarter of 2003.

The second variable, modelled-based expectations, is generated on the basis of adaptive expectation hypothesis. This paper assumes market players extrapolate from the price history of the most recent three years. The same time frame is used by Phillips (1988). Furthermore, Lam and Hui (2018) find that investors’ sentiment is affected by the returns of investment properties with a lagged effect from three to twelve months. In short, based on our understanding of the Auckland housing market, we make an assumption that investors’ one-year ahead price expectations are backward-looking for three years. This paper adopts the method used by Boudoukh et al. (1998) to generate decreasing weights to the housing price changes in the most recent three years. The purpose of using declining weights is to reflect the fact that market participants tend to lay more emphasis on recent price dynamics and less importance on distant price fluctuations. The approach we adopt to model one-year ahead price expectorations, P*, are expressed by the following equation (1):

P^∗= ∆P₁∙ 1 − λ

1 − λ^k+ ∆P₂∙ 1 − λ

1 − λ^k∙ λ + ⋯ + ∆P₁₂∙ 1 − λ

1 − λ^k∙ λ^k−1 (1)

In equation (1), the housing expectations, P*, is expressed as a function of age-weighted historical price fluctuations. ∆P is the price change based on the Auckland housing price index. λ is the decay factor. k is equal to 12 in our case because we assume that property buyers extrapolate from three-year actual price movements with higher weights being placed on recent changes.

To use the approach proposed by Boudoukh et al. (1998), a value of the decay factor, λ, has to be set.

Žiković and Prohaska (2010) test the optimal values of the decay factor in nine European stock markets and they conclude that, for both developed and developing nations, the optimal values are similar, ranging from 0.991 to 0.998. This study, therefore, takes the midpoint of 0.991 and 0.998 as the value of λ.

The third variable, survey-based expectations, is sourced from the ASB Housing Confidence Survey.

The ASB is one of the “big four banks” in New Zealand. The survey is conducted by the bank on a quarterly basis and asks participants the following three questions:

1. whether or not now is a good time to buy (a property)?

2. whether (housing) prices will increase or decrease in the next twelve months?

3. what direction future interest rates will go?

The sample size is usually about 600 and at that size has a margin of error of about 4%². Lambertini et al. (2013) and Towbin and Weber (2015) use the results of the first question—whether or not now is a good time to buy (a property)?—from the Michigan Survey of Consumer. They focus on the proportion of survey participants who think the timing is good and include the share in modelling. We think that

2 https://www.interest.co.nz/charts/confidence/housing-confidence

the timing is a reflection of market players’ perceptions of current housing markets. Participants’

response regarding future price dynamics should be a better indicator of housing price expectations.

Therefore, we take the net percentage of the survey respondents who believe Auckland housing prices will increase in the next twelve months. In order to obtain the original data, we have made attempts to contact ASB but the bank has rejected our request. We have to resort to “reverse engineering”: we have extracted data from a graph showing the net percentage of the survey participants who think Auckland housing price will increase in the next twelve months. A grid is placed over Figure 3 to figure out the net portions from 2003 to 2015. The net shares in 2016, however, are reported directly by the 2016 surveys so “reverse engineering” is not used to get the 2016 data.

Figure 3. ASB Housing Confidence Survey.

Source: ASB Housing Confidence Survey May 2016.

Effective mortgage rates are downloaded from the Reserve Bank of New Zealand (RBNZ). The effective mortgage rates are weighted averages of the interest rates currently being paid across all types

of mortgage lending issued by banks in New Zealand³. The RBNZ reports effective mortgage rates on a monthly basis. We have converted monthly rates into quarterly ones by taking the monthly rates in January, April, July and October as the rates in the first, second, third and fourth quarter.

The number of building consents issued in Auckland refers to the quantity of new residential building consents granted in the Auckland region. The variable is downloaded from the Statistics New Zealand, the public service department of New Zealand charged with the collection of statistics. To reduce the degree of seasonality, we have taken three-month moving averages to smooth the variable. Furthermore, a one-year lag is applied to the number of building consents issued in Auckland because, for typical freestanding houses, most construction work is completed within 12 months of the issuance of a building consent (Statistics New Zealand, 2017). In short, we have adopted the variable as a proxy for housing supply. Auckland Council figures have suggested that the number of building consents issued in Auckland is a reasonable indicator of housing supply in the housing market⁴.

The number of the net permanent and long-term immigrants entered into New Zealand is used as a proxy for housing demand. The variable, which has been seasonally adjusted, is directly downloaded from the Statistics New Zealand. The descriptive statistics of all series from 2006 to 2016 are reported in Table 1⁵.

3 https://www.rbnz.govt.nz/statistics/b3

4 https://www.interest.co.nz/property/95188/auckland-council-figures-suggest-around-98-new-dwellings-consented-region- actually

5 We make an assumption that investors’ one-year ahead housing price expectations are backward-looking for three years.

Therefore, to generate MBE with the three-year time frame, HP from 2003 to 2005 have to be truncated. All six series from 2006 to 2016 are reported in Table 1 and included in the following vector error correction models.

Table 1. Descriptive statistics of the variables

Variable Average Std.dev. Minimum Maximum

Auckland housing price index (HP) 776 259 496 1,343

Modelled-based expectations (MBE) 0.0106 0.0073 -0.0021 0.0238

Survey-based expectations (SBE) 0.3116 0.3492 -0.5300 0.7200

Effective mortgage interest rates (IR) 0.0667 0.0117 0.0493 0.0880

The number of building consents issued in Auckland (BC) 1,455 498 763 2,495 Net permanent and long-term immigrants entered into New Zealand (IM) 5,959 6,434 -1,100 18,250

Unit Root Tests

This paper adopts two Vector Error Correction Models (VECM) as frameworks to analyse the relationship between MBE and HP as well as the relationship between SBE and HP. The first model includes MBE, HP, IR, BC and IM and the second model encompasses SBE, HP, IR, BC and IM. There is a set of procedures to follow in order to build a VECM and the very first step is to check whether data are stationary. If they are nonstationary, a problem known as spurious regression may happen and this issue further leads to misleading and meaningless statistical evidence. To test whether data are stationary, Augmented Dickey-Fuller (ADF) tests are conducted.

Table 2. The results of ADF tests

Variable Level First difference

Test statistic (5%, 1% CV) P value Test statistic (5%, 1% CV) P value

HP 2.55 (-2.93, -3.59) 1.000 -3.17 (-2.93, -3.60) 0.029

MBE -1.07 (-1.95, -2.62) 0.254 -2.24 (-1.95, -2.63) 0.026

SBE -2.17 (-3.52, -4.19) 0.494 -5.13 (-3.52, -4.19) 0.000

IR -0.66 (-2.94, -3.60) 0.845 -3.29 (-2.94, -3.60) 0.022

BC -0.82 (-3.54, -4.23) 0.954 -3.62 (-3.53, -4.22) 0.041

IM -1.41 (-3.52, -4.19) 0.843 -4.58 (-3.52, -4.19) 0.004

Table 2 reports the results of a series of ADF tests. It can be seen that all data are non-stationary at level but stationary at a significance level of 5% after taking the first difference, a situation defined as being integrated of order one I (1). Therefore, we establish our VECM models using the first difference.

Equation (2) and (3) are a general form of a VECM model including two differenced variables.

∆X_𝑡 = α₁+ β₁₁∆X_𝑡−1+ ⋯ + β_1𝑝∆X_𝑡−𝑝+ θ₁₁∆Y_𝑡−1+ ⋯ + θ_1𝑝∆Y_𝑡−𝑝+ μ_1𝑡 (2)

∆Y_𝑡 = α₂+ β₂₁∆X_𝑡−1+ ⋯ + β_2𝑝∆X_𝑡−𝑝+ θ₂₁∆Y_𝑡−1+ ⋯ + θ_2𝑝∆Y_𝑡−𝑝+ μ_2𝑡 (3)

In equation (2) and (3), ∆X and ∆Y are variables with the first difference. Β and θ represent corresponding coefficients. α is the intercept and μ describes the error term or disturbance. p is the optimal lag length.

Lag Length Selection

It is necessary to include lags in our VECM models because historical fluctuations in variables may affect other variables in the future. In our case, past price changes are very like to exert influence on investors’ decisions about whether or not to buy. An inappropriate lag length falsely reflects the impact of a lagged variable on other variables. In addition, an incorrect selection of a lag makes a model over- parameterised or over-simplified (Chen and Patel, 1998). There are several information criteria that help select the optimal lag length including sequential modified LR test statistic (LR), final prediction error (FPE), Akaike information criterion (AIC), Schwarz information criterion (SIC) and Hannan- Quinn information criterion (HQC). Ivanov and Kilian (2005) find that HQC is the most accurate criterion for quarterly VAR models with the exception of a sample size small than 120, for which SIC is more accurate. According to SIC, the optimal lag length of our two VECM models is 2.

Cointegration and Granger Causality Test

According to the results of our ADF tests, all differenced variables are stationary at a significance level of 5% and this signals the presence of at least one cointegrating relationship among variables. Johansen

cointegration tests are used to examine the number of cointegrating equations. By conducting the tests, we have discovered that the VECM model with MBE and the model with SBE have two and one cointegrating relations, respectively. If cointegration exists, a VAR model with differenced variables is incompatible with cointegrated systems because it omits an error correction term (Engle and Granger, 1987).In our case, as cointegration has been found, we can establish two VECM models using the ordinary least squares (OLS) method.

VECM models are used as frameworks to analyse the relationship between MBE and HP as well as the relationship between SBE and HP. To study the relationships, Granger causality tests and impulse response analyses are conducted. If a variable X contains exclusive information in the past that helps in the forecast of another variable Y, then X is said to Granger cause Y (Granger, 1969). When two variables Granger cause each other, a bidirectional Granger causality forms. Granger causality is not real causation but indicates that the past value of a variable has statistical explanatory power of the future value of another variable. The advantage of Granger causality is that it measures statistically causal effects in a simple, clear and unambiguous way on stationary VAR processes (Barrett and Barnett, 2013).

Granger causality tests reveal little about whether the response of a variable to a shock is positive or negative. They cannot demonstrate if the impact of a variable on others is a short-run jump or long-term effects. We, therefore, decide to add impulse response analyses as a supplement to Granger causality tests. An impulse response function examines the response of the present and future values of an endogenous variable to one standard deviation shock (Hui and Yue, 2006). The impulse response analysis unveils the direction of the impact of a shock and how long its effect lasts. An impulse response analysis with shocks being orthogonalised using the Cholesky decomposition method is variant to the ordering of the variables in a VECM (Pesaran and Shin, 1998). To overcome this flaw, we conduct

generalised impulse response analyses that are not subject to a change of sequence of the variables. An orthogonalisation of shocks, therefore, is not needed.

4. Results

Granger causality tests

Table 3 reports the results of Granger causality tests. It can be noted that MBE Granger causes HP and the latter Granger causes the former at the 10% level of significance, indicating a bidirectional Granger causality between the two variables. With our modelled expectation, the finding demonstrates a feedback cycle: realised housing capital gains lift expectations which in turn spur price growth. A one- way Granger causal relationship has been identified from IR to MBE, suggesting that mortgage interest rates provide statistically significant information about modelled expectations. In addition, at 10%

significance level, BC Granger causes both HP and MBE, indicating that Auckland housing supply has a weak relationship with housing price and modelled expectations. This finding is in line with the statement made by Kitchin et al. (2015). They claim that a construction boom in Ireland from 1991 to 2006 has led to a surge in housing prices partly because new development is largely bought by speculative buyers.

Table 3. The results of Granger causality tests

H0 X² P value H0 X² P value H0 X² P value H0 X² P value

MBE→HP 10.472 0.005 HP→MBE 5.901 0.052 SBE→HP 0.703 0.704 HP→SBE 9.633 0.008

IR→HP 1.215 0.545 IR→MBE 7.458 0.024 IR→HP 2.886 0.236 IR→SBE 2.579 0.275

BC→HP 5.036 0.081 BC→MBE 4.718 0.095 BC→HP 0.949 0.622 BC→SBE 1.871 0.392

IM→HP 0.589 0.745 IM→MBE 1.210 0.546 IM→HP 3.746 0.154 IM→SBE 4.646 0.098

Note: x→y indicates the null hypothesis that x does not Granger cause y.

With survey-based expectations, no one-way Granger causality has been found from other variables to HP. HP, however, Granger causes SBE at 1% significance level, indicating that investors’ expectations

are firmly based on housing price fluctuations. In addition, the number of immigrants also plays a role in the formation of expectations because IM Granger causes SME at 10% significance level.

According to the results of our Granger causality tests, we can draw a conclusion that Auckland property buyers’ expectations are highly adaptive because housing prices have statistically significant explanatory power of both modelled expectations as well as adaptive expectations. The p-value of HP→SBE is much smaller than that of HP→MBE, indicating that buyers’ expectations are actually more adaptive than we assume. In addition, our modelled expectation, MBE, Granger causes HP but the survey-based expectation, SBE, does not Granger cause HP. This finding shows that expectations are not a determinant of Auckland housing prices.

Generalised Impulse Response Analyses

Figure 4 presents the response of housing prices to endogenous variables including MBE. A standard deviation of Auckland housing prices itself positively affects future housing prices, suggesting that an increase in current housing prices elevates investors’ expectations. Modelled expectations are the second most influential variable as the reaction of housing prices to a shock of modelled expectations is positive with a peak of 73% in three years. An increase in the number of immigrants has a neutral effect on housing prices in the first quarter. The effect, however, turns positive in the second quarter and gradually grows large. This result suggests that it takes immigrants two-quarter time to generate housing demand that adds upward pressure to housing prices. Lastly, the impact of mortgage interest rates on housing prices is negative, meaning that a rise in interest rates curbs house price escalation.

Figure 4. The response of HP to endogenous variables including MBE

Figure 5 demonstrates the response of modelled expectations to endogenous variables. The impact of housing prices on modelled expectations is positive with a minimum response of 0.0008% in the sixth quarter. The effect of the number of building consents is initially negative in the first two quarters and turns positive in the following quarters. The response of modelled expectations to a shock of immigration is positive. This finding is consistent with the fact that housing price expectations increase when more immigrants enter into a housing market. Lastly, a standard deviation increase in interest rates leads to a 0.0005% decrease in expectations in the first quarter, meaning that a rise in interest rates disheartens market players.

Figure 5. The response of MBE to endogenous variables

Figure 6 illustrates the response of housing prices to endogenous variables including SBE. Survey- based expectations have a positive impact on housing prices with the magnitude gradually becoming large. Consistent with the finding in Figure 4, an increase in the number of immigrants has a neutral effect on housing prices in the first quarter. The effect, however, turns positive in the second quarter.

This result suggests that it takes immigrants two-quarter time to generate housing demand that adds upward pressure to housing prices. The response of housing prices to a shock of mortgage interest rates is negative, meaning that a rise in the rates dampens house price escalation.

Figure 6. The response of HP to endogenous variables including SBE

Figure 7 shows the response of survey-based expectations to endogenous variables. A standard deviation increase in housing prices leads to a rise in the expectations with a sharp decline occurring starting from the third quarter. This finding implies that a change in housing prices dramatically affects expectations during the first three quarters and its influence becomes much smaller from the fifth quarter.

The impact of immigration on the expectations is negative during the first three quarters, turns positive in the fourth quarter and becomes negative again in the ninth quarter. Overall, the number of immigrants has a mixed impact on survey-based expectations. The reaction of the expectations to mortgage interest rates is negative with a minimum response of 0.13% in the sixth quarter. A change in the mortgage rate exerts its greatest effect after six quarters and then the magnitude declines.

Consistent with the results of our Granger causality tests, impulse response analyses (Figure 4, Figure 5, Figure 6 and Figure 7) demonstrate the same feedback cycle: realised housing capital gains lift expectations which in turn spur price growth.

Figure 5 and Figure 7 shows how adaptive expectations and survey-based expectations respond to housing price changes. By comparing Figure 5 and Figure 7, we can find that housing prices positively affect both two forms of expectations. Furthermore, there are some similarities between the shapes of two solid black HP lines: they are both M-shaped. Consistent with the results of our Granger causality tests, we can draw a conclusion that Auckland property buyers’ expectations are adaptive.

Figure 7. The response of SBE to endogenous variables

5. Conclusion

This study examines whether Auckland property buyers’ expectations are adaptive by building two VECM models with two forms of expectations: modelled expectations and survey-based expectations.

It also investigates the relationship between housing prices and model-based expectations as well as the relationship between housing prices and survey-based expectations.

The results of Granger causality tests demonstrate that housing prices provide statistically significant explanatory power of both model-based and survey-based expectations, indicating that Auckland property owners form their expectations based on historical price movements. The findings of impulse response analyses also suggest that the buyers’ expectations are adaptive because the reaction of survey- based expectations to housing prices is positive.

According to the results of Granger causality tests, there is a one-way Granger causality from housing prices to survey-based expectations, meaning that housing prices are a determinant of expectations. The impulse response analyses, however, show that there is a feedback cycle between the two variables:

housing prices and survey-based expectations positively affect each other. We can conclude that there may be a spiralling movement cycle: realised housing capital gains lift expectations which in turn spur price growth.

The findings of this study can be useful for several parties. For example, the RBNZ has been keeping a close eye on Auckland housing prices and the volume of mortgages taken by investors with a high level of loan-to-value ratio (LVR). This paper helps policymakers to deepen their understanding of property buyers by showing that investors’ expectations form through the extrapolation of past price trend and they are sensitive about a rise in mortgage interest rates. From the perspective of investors, the feedback cycle between housing prices and survey-based expectations is significant for market forecasting. Our findings suggest that data from the ASB Housing Confidence Survey are an important market indicator of future price movements.

Admittedly, this study is subject to a number of limitations. First, the survey-based expectations are based on the net percentage of the respondents who believe Auckland housing prices will increase in the next twelve months. Data built upon a survey that asks participants about what percentage housing prices will increase or decrease may be a better proxy for expectations. Furthermore, since this paper

focuses on twelve-month expectations, further research may test the relationship between long-term expectations and housing prices. The ANZ Property Investment Survey (2017) has revealed that, in term of capital gains, investors are more optimistic in the long run than in the short run. Second, we set a value of the decay factor, λ, according to the finding of Žiković and Prohaska (2010). They find the optimal value of λ based on the empirical evidence of several European stock markets. However, the Auckland housing market may have different characteristics that make our value of the decay factor inappropriate. To our knowledge, the optimal value of λ has not been tested in housing markets so this area may be of interest to some researchers. Three, by assuming that property buyers extrapolate from the most recent three-year price movements, this study ignores distant price history that may affect expectations. Past price fluctuations, especially booms and crashes, are reported to cause behavioural differences (Fredrickson, 2000; Gong et al., 2013). Therefore, an additional dimension to explore is how prices history, particularly large price swings, shapes investors’ expectations.

References

ANZ (2017). NZ Residential Property Investment. Retrieved from

https://www.interest.co.nz/sites/default/files/embedded_images/ANZ%20Property%20Investor%20Re port%2020170904.pdf

Bailey, M., Cao, R., Kuchler, T., & Stroebel, J. (2018). The economic effects of social networks:

Evidence from the housing market. Journal of Political Economy, 126(6), 2224-2276.

Barrett, A. B., & Barnett, L. (2013). Granger causality is designed to measure effect, not mechanism.

Frontiers in neuroinformatics, 7, 6.

Boudoukh, J., Richardson, M., & Whitelaw, R. (1998). The best of both worlds. Risk, 11(5), 64-67.

Brown, P. M. (1990). United Kingdom residential price expectations and inflation. Land Development Studies, 7(2), 57-67.

Cadil, J. (2009). Housing price bubble analysis–case of the Czech Republic. Prague Economic Papers, 1, 38-47.

Case, K. E., & Shiller, R. J. (1988). The behavior of home buyers in boom and post-boom markets.

Chen, M. C., & Patel, K. (1998). House price dynamics and Granger causality: an analysis of Taipei new dwelling market. Journal of the Asian Real Estate Society, 1(1), 101-126.

Chow, G. C. (2011). Usefulness of adaptive and rational expectations in economics. Center for Economic Policy Studies, Princeton University.

Clapham, E., Englund, P., Quigley, J. M., & Redfearn, C. L. (2006). Revisiting the past and settling the score: index revision for house price derivatives. Real Estate Economics, 34(2), 275-302.

Diamond, D. B. (1980). Taxes, inflation, speculation and the cost of homeownership. Real Estate Economics, 8(3), 281-298.

Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251-276.

Fredrickson, B. L. (2000). Extracting meaning from past affective experiences: The importance of peaks, ends, and specific emotions. Cognition & Emotion, 14(4), 577-606.

Geltner, D., & Ling, D. (2006). Considerations in the design and construction of investment real estate research indices. Journal of Real Estate Research, 28(4), 411-444.

Gong, B., Lei, V., & Pan, D. (2013). Before and after: The impact of a real bubble crash on investors’

trading behavior in the lab. Journal of Economic Behavior & Organization, 95, 186-196.

Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424-438.

He, W., & Shen, J. (2010). Investor extrapolation and expected returns. Journal of Behavioral Finance, 11(3), 150-160.

Hou, Y. (2010). Housing price bubbles in Beijing and Shanghai? A multi-indicator analysis.

International Journal of Housing Markets and Analysis, 3(1), 17-37.

Hui, E. C., & Yue, S. (2006). Housing price bubbles in Hong Kong, Beijing and Shanghai: a comparative study. The Journal of Real Estate Finance and Economics, 33(4), 299-327.

Ihlanfeldt, K. R., & Shaughnessy, T. M. (2004). An empirical investigation of the effects of impact fees on housing and land markets. Regional Science and Urban Economics, 34(6), 639-661.

Ilmakunnas, P. (1989). Survey expectations vs. rational expectations in the estimation of a dynamic model: demand for labour in Finnish manufacturing. Oxford Bulletin of Economics and Statistics, 51(3), 297-314.

Ivanov, V., & Kilian, L. (2005). A practitioner's guide to lag order selection for VAR impulse response analysis. Studies in Nonlinear Dynamics & Econometrics, 9(1).

Kim, C. H., & Kim, K. H. (1999). Expectation and housing price dynamics following deregulation in Korea. International Real Estate Review, 2(1), 126-142.

Kim, K. H., & Suh, S. H. (1993). Speculation and price bubbles in the Korean and Japanese real estate markets. The Journal of Real Estate Finance and Economics, 6(1), 73-87.

Kitchin, R., Hearne, R., & O'Callaghan, C. (2015). Housing in Ireland: From crisis to crisis. Available at SSRN 2566297.

Lam, C. H. L., & Hui, E. C. M. (2018). How does investor sentiment predict the future real estate returns of residential property in Hong Kong? Habitat International, 75, 1-11.

Lambertini, L., Mendicino, C., & Punzi, M. T. (2013). Expectation-driven cycles in the housing market:

Evidence from survey data. Journal of Financial Stability, 9(4), 518-529.

Levin, E. J., & Wright, R. E. (1997). Speculation in the housing market? Urban Studies, 34(9), 1419- 1437.

Ling, D. C., Ooi, J. T., & Le, T. T. (2015). Explaining house price dynamics: Isolating the role of nonfundamentals. Journal of Money, Credit and Banking, 47(S1), 87-125.

Lucas R. E. (1976). Econometric policy evaluation: A critique. In Carnegie-Rochester conference series on public policy (Vol. 1, pp. 19-46). North-Holland.

Malpezzi, S., & Wachter, S. (2005). The role of speculation in real estate cycles. Journal of Real Estate Literature, 13(2), 141-164.

Mohan, S., Hutson, A., MacDonald, I., & Lin, C. C. (2019). Impact of macroeconomic indicators on housing prices. International Journal of Housing Markets and Analysis.

Muth, J. F. (1961). Rational expectations and the theory of price movements. Econometrica: Journal of the Econometric Society, 315-335.

Nordvik, V. (1995). Prices and price expectations in the market for owner occupied housing. Housing Studies, 10(3), 365-380.

Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models.

Economics letters, 58(1), 17-29.

Phillips, R. S. (1988). Residential capitalization rates: Explaining intermetropolitan variation, 1974–

1979. Journal of Urban Economics, 23(3), 278-290.

Shi, S., Young, M., & Hargreaves, B. (2010). House price–volume dynamics: evidence from 12 cities in New Zealand. Journal of Real Estate Research, 32(1), 75-99.

Statistics New Zealand. (2017), Building plans put to work. Available from https://www.stats.govt.nz/assets/Reports/Building-plans-put-to-work/building-plans-put-to-work.pdf

Sommervoll, D. E., Borgersen, T. A., & Wennemo, T. (2010). Endogenous housing market cycles.

Journal of banking & finance, 34(3), 557-567.

Taltavull, P., Kuang, W., & Li, X. (2012). Does China face a housing affordability issue? Evidence from 35 cities in China. International Journal of Housing Markets and Analysis.

Towbin, P., & Weber, M. S. (2015). Price expectations and the US housing boom (No. 15-182).

International Monetary Fund.

Wong, J. T., & Hui, E. C. (2006). Research notes–power of expectations. Property Management, 24(5), 496-506.

Wong, J. T., Hui, E. C., Seabrooke, W., & Raftery, J. (2005). A study of the Hong Kong property market:

housing price expectations. Construction Management and Economics, 23(7), 757-765.

Žiković, S., & Prohaska, Z. (2010). Optimisation of decay factor in time weighted (BRW) simulation:

implications for VAR performance in mediterranean countries. Economic research, 23(1), 73-85.