1. CHIEN-WEN PENG NATIONAL TAIPEI UNIVERSITY I-CHUN TSAI NATIONAL UNIVERSITY OF KAOHSIUNG STEVEN BOURASSA UNIVERSITY OF LOUISVILLE 06/25/ 2010 Determinants of Long-Run Homeownership Rates: Evidence from Taiwan

2. Homeownership Rate Accumulated results of individual household’s housing tenure choice.

3. Benefits of Homeownership Positive impacts on people’s behavior, especially during the childhood. (Green and White 1997; Haurin et al. 2002; Lien et al. 2008)  higher test scores Increase people’s attachment to their property and community, which tends to have stabilizing effect on society. (Rossi and Weber 1996; Dipasquale and Glaeser 1999)  better neighbor, better citizen

4. Policies to Promote Homeownership Rate Supply Side Subsidy  Affordable Public Housing Demand Side Subsidy  Preferential Interest Mortgage  Mortgage Interest Deduction from Income Tax  Lower Property Tax Rate  Lower down payment Required (Higher LTV)

5. Costs of Homeownership Obscure costs with respect to Limited economic resource allocation Economic development Housing market operation

6. Homeownership Rates in US-1965~2008 63.4% 67.5% +4.1%

7. Case & Shiller House Price Index-1987~2009 62.03 189.93 132.64 +206.2% -30.16%

8. House Price and Homeownership Rate House Price  Relative Cost of Owning vs. Renting  House Price Affordability (wealth and income constrains) House price ↑  User Cost of Owning ↑  Affordability ↓  Ownership Rate ↓ Exp. House Price Appreciation ↑  Ownership Rate ↑

9. Homeownership Rates and House Price in US Positive or Negative?

10. Painter and Redfearn(2002)  Interest rates had an influence on both housing supply and timing of changes of tenure status from renter to owner, the long- term homeownership rate appears independent of interest rates.  To promote homeownership rates, low down payment and improved technology for assessment of credit risk may be more effective.

11. Homeownership Rates in Taiwan:1976~2008 +20% 87.4% 67.4%

12. Ownership Rates in Taiwan and USA-1976~2008 64.8% 67.5% +2.7% +20% 67.4% 87.4%

13. Ownership Rates and House Price of Taipei City

14. Ownership Rates and House Price of Taipei County

15. Ownership Rates and House Price of Taichung City

16. Ownership Rates and House Price of Kaohsiung City

17. Research Questions Both of the patterns of long-run homeownership rates and house prices in US. and Taiwan are strange. What are the determinants of long-run homeownership rates? (Does it implies Taiwan’s homeownership promotion policies are more effective than U.S.? )

18. Literature Review Abundant Literature on Determinants of Individual Household’s Tenure Choice Some studies focus on Homeownership Rates Differences in different Nations /Regions Rare on the Determinants of Long- run Homeownership Rates

19. Tenure Choice- Market Factors  Housing Price, HP Fluctuation Risk ↑  Rent  Borrowing Constrains (LTV↓, Interest Rate↑) ↑  Rent  Rent, Rent Fluctuation Risk ↑  Buy  Expected Housing Price Appreciation ↑  Buy

20. Tenure Choice- Institution Factors  Property Tax ↑  Rent  Relative Cost of Owning vs. Renting ↑  Rent  Deduction of Mortgage Interest from Income Tax↑  Buy  Owner-occupied Housing Subsidies↑  Buy

21. Tenure Choice- Household’s Characteristics  Expected Mobility ↑  Rent  Household Income↑  Buy  Household Head’s Age↑, Married  Buy  Family Size ↑  Buy  Number of Dependent Children↑  Buy

22. Selected Variables (no institutional factors ) House Price (p) Household Income (I) House Price to Income Ratio (pI) Rent Growth Rate (red) House Price Growth Rate (pd) Income Growth Rate (ld) Household Growth (h) Mobility Rates (mov) Proportion of Married Couples (mar) Proportion of Elderly People (old)

23. Empirical Study Investigate the Determinants of Long-Run Homeownership Rates Data: Taipei City, Taipei County, Taichung City, Kaohsiung City, 1980~2007,Sample Size 112 Methodology: Panel Co-integration

24. Panel Co-integration Cointegration is an econometric property of time series variables.econometrictime series If two or more series are themselves non-stationary, but a linear combination of them is stationary, then the series are said to be cointegrated.stationary Panel Co-integration = Cross Section + Time Series  More Samples, More Information

25. Panel Unit Root Test IPS ADF-Fisher

26. Variable Panel Unit Root Test IPSADF - Fisher Chi-square Levels own 0.279.62 mar 7.080.12 mov -1.3521.53*** old 6.550.19 h -1.5813.32 p 0.016.03 I -0.105.56 pI -1.1910.75 pd -2.00**17.70** Id -4.92***38.68*** red -0.678.22

27. Variable Panel Unit Root Test IPSADF - Fisher Chi-square Differences △ own -13.23***105.03*** △ mar -6.09***48.18*** △ mov -8.49***69.03*** △ old -6.37***49.81*** △h△h -9.62***77.28*** △p△p -2.05**17.65** △I△I -5.71***45.45*** △ pI -5.70***44.67*** △ pd -11.70***88.14*** △ Id -7.38***61.41*** △ red -4.22***32.36***

28. Results of Panel Unit Root Test Can not reject the null hypothesis of having a unit root for the levels of most variables, except house price appreciation rate (pd) and income growth rate (Id). The differences of all variables are significantly to reject the null hypothesis which implies most variables are I(1).

29. own and mar mov old h (demographic) own and I, p, pI (affordability) own and red (consumption) Model 1 without trend Model 2 with trend Panel Co-integration Test

30. Panel Statistics Weighted Panel Statistics Group Statistics Series: own mar mov old h PP statistic -4.605***-4.605 *** -6.181 ADF statistic -4.519***-4.519 *** -6.048 Series: own mar PP statistic -2.875***-3.336***-3.652 ADF statistic -2.499**-3.031***-3.334 Series: own mov PP statistic -3.161 *** -3.339 *** -3.504 ADF statistic -3.118 *** -3.295 *** -3.455 Series: own old PP statistic -4.875 *** -4.821 *** -5.551 ADF statistic -5.269 *** -5.416 *** -5.418 Series: own h PP statistic -0.436-0.658-0.065 ADF statistic -0.385-0.7260.084

31. Panel Statistics Weighted Panel Statistics Group Statistics Series: own p I PP statistic -3.563 *** -2.823 *** -3.248 *** ADF statistic -4.160 *** -3.856 *** -4.338 *** Series: own p PP statistic -1.723-1.919-1.054 ADF statistic -1.674-1.860-1.043 Series: own I PP statistic -4.203 *** -2.986 *** -3.736 *** ADF statistic -3.644 *** -3.089 *** -3.247 *** Series: own pI PP statistic 0.9191.2462.053** ADF statistic 1.1951.3052.190**

32. Panel Statistics Weighted Panel Statistics Group Statistics Series: own red PP statistic -0.421-0.2160.464 ADF statistic -0.523-0.3140.361

33. Panel Co-integration Test without trend Long-run equilibrium relationship between own and I, mar, old, mov No cointegration relationship between own and h, p, red

34. own and mar mov old h (demographic) own and I, p, pI (affordability) own and red (consumption) Panel Co-integration Test -With Trend

35. Panel Statistics Weighted Panel Statistics Group Statistics Series: own mar mov old h PP statistic -6.77 *** -7.02***-8.20 *** ADF statistic -6.71 *** -6.70***-6.39 *** Series: own mar PP statistic -5.95***-5.99***-5.76*** ADF statistic -5.93***-5.97***-5.72*** Series: own mov PP statistic -5.30***-5.12***-4.94*** ADF statistic -5.28***-5.12***-5.00*** Series: own old PP statistic -6.92***-6.17***-6.52*** ADF statistic -6.92***-6.17***-6.48*** Series: own h PP statistic -5.47 *** -4.43 *** -4.98 *** ADF statistic -5.49 *** -4.49 *** -5.05 ***

36. Panel Statistics Weighted Panel Statistics Group Statistics Series: own p I PP statistic -7.51 *** -6.76 *** -8.65 *** ADF statistic -7.33 *** -6.61 *** -7.41 *** Series: own p PP statistic -8.08 *** -8.91 *** -7.82 *** ADF statistic -8.03 *** -8.76 *** -7.47 *** Series: own I PP statistic -7.08 *** -5.19 *** -6.88 *** ADF statistic -7.93 *** -6.38 *** -6.68 *** Series: own pI PP statistic -5.63 *** -4.82 *** -5.31 *** ADF statistic -5.61 *** -4.83 *** -5.39 ***

37. Panel Statistics Weighted Panel Statistics Group Statistics Series: own red PP statistic -7.28 *** -6.79 *** -6.55 *** ADF statistic -7.29 *** -6.79 *** -6.57 ***

38. Panel Co-integration Test with trend All variables have cointegration relationships with homeownership rates. A trend in homeownership rate serial.

39. FMOLS_ Taipei City variablecoefficientt value MAR2.417.19 MOV0.251.30 OLD4.028.15 H-0.09-0.33 P0.161.81 I0.010.56 RED0.071.09

40. FMOLS_ Taipei County variablecoefficientt value MAR-0.23-1.19 MOV0.282.48 OLD4.775.36 H-0.61-2.77 P0.221.47 I-0.12-2.90 RED-0.07-1.02

41. FMOLS_ Taichung City variablecoefficientt value MAR-0.44-1.10 MOV-0.31-1.06 OLD-0.42-0.24 H-0.44-1.31 P0.491.04 I0.163.84 RED-0.02-0.08

42. FMOLS_ Kaohsiung variablecoefficientt value MAR0.770.23 MOV-0.07-0.18 OLD2.760.55 H0.250.36 P-0.04-0.09 I0.222.06 RED0.240.97

43. FMOLS_ Panel variablecoefficientt value MAR0.632.57 MOV0.041.27 OLD2.786.90 H-0.22-2.02 P0.212.12 I0.071.78 RED0.060.48

44. Results of FMOLS the most influential variables of own are different in the four cities. Taipei City: old(+), mar(+), p(+) Taipei County: old(+), mov(+), l(-), h(-) Taichung City and Kaohsiung City: I (+) In General, old, mar, p, I (+), h (-)

45. Conclusions A trend exists in Taiwan’s homeownership rates, not explainable by selected variables which may contributed to the influence of institutional factors. If not consider the trend, long-run equilibrium relationships only between ownership rates and  household income  proportion share of married couples  Proportion of elderly people  mobility rates

46. Conclusions If consider the trend, can find co-integration between homeownership rates and house prices, household growth rate, rent growth rate. From FMOLS, the most influential variables of own are different in the four cities. In general, proportion of elderly people, proportion of married couple, house price are most influential vars.

47. Policies Implications Why there is a trend in Taiwan’s homeownership rates? Possible explanation: Low owning cost which due to low property tax and high expectation of house price appreciation, especially in Taipei City  effective property tax rate↑  better rental housing market

48. Thanks for your Attention