HOUSE PRICE DETERMINANTS IN SYDNEY

Transcription

1 Dr Xin Janet Ge (PhD, MBA, Bcom, MRICS, AAPI, CPP(Ed)) School of the Built Environment, University of Technology Sydney Dr Brendan Williams Department: School of Geography, Planning and Environmental Policy, University College Dublin HOUSE PRICE DETERMINANTS IN SYDNEY UTS CRICOS PROVIDER CODE: 00099F UTS:DESIGN, ARCHITECTURE & BUILDING

2 CONTENTS House price performance in Australia Drivers of house price appreciation Empirical study Results and Conclusion

3 HOUSE PRICE PERFORMANCE IN AUSTRALIA UTS CRICOS PROVIDER CODE: 00099F UTS:DESIGN, ARCHITECTURE & BUILDING

4 Sydney Median Price Times 1985 Interest can be claimed against rental income Median Family Income NSW Weekly 1987 Negative gearing reintroduced Times Jun 14 $ 811,840 $ 1,562 Jun 04 $ 552, $ 1, Jun 94 $ 194, $ Jun 84 $ 84, First home owners grant $7000 for established and $14000 for new home 1999 Capital Gains Tax reduced from 100 to 50% & 100% costs deductible 2002 Urban growth boundary in Mel. Mining boom 2003 Significant amendments QLD planning law The 1 st home owners grant boost (FHOGB) ($ $7000) + $850 pa savings deposit Temporary visa holders can buy old dwellings FHOGB removed mortgage application reduced by 21.2% Cash rate cuts to 2.5% New home loan 5.6%, 10 yr low Removed foreign investment rules in required to sell property when leaving Australia

5 SYDNEY RESIDENTIAL PROPERTY PRICES Source: ABS: 6416; REIA

6 KEY DRIVERS UTS CRICOS PROVIDER CODE: 00099F UTS:DESIGN, ARCHITECTURE & BUILDING

7 KEY DRIVERS OF HOUSE PRICE APPRECIATION Housing is shelter, convenience, and social distinction (Muth & Goodman, 1989). House prices are determined by demand for and supply of housing (Rahman, 2015). Demographic effects and population growth are major determinants of household consumption patterns (Pollak & Wales, 1981; Megbolugbe & Cho, 1993; Rosen, 1979; Turner & Struyk, 1984; Anas & Eum, 1984; Haurin & Gill, 1987; Goodman, 1990; Meen, 1995; Ho & Ganesan, 1998; Manning, 1989; Potepan, 1994; Dieleman et al., 2000). Muth (1960) concluded that housing demand is highly responsive to changes in income and price. Holly and Jones (1997); Abelson (1997); Brown et al. (1997); Goodman (1988) had similar findings. Availability of credit and interest rate (Pozdena, 1988; Sven & François, 2001; Omar & Ruddock, 2002) Speculation (Case & Shiller, 1989, 1990; Ito & Hirono, 1993; Levin & Wright, 1997; Abraham and Hendershott, 1996; Meen, 1998; Muellbauer and Murphy, 1997; Boelhouwer et al., 1996, 2000; Golland and Boelhouwer, 2002; and Ho, 1998, ) Other factors: location, neighbourhood, and racial, historical, physical and structural factors determine house prices (Maclennan, 1982) Abelson (1997) showed similarly that house prices are determined mainly by the distance from the CBD, house and lot size, and other housing attributes including local environmental amenities. Supply factors (Nellis & Longbottom, 1981; Maclennan, 1982; Megbolugbe & Cho, 1993; Baer, 1986; Dipasquale, 1999; Ley & Tutchener, 2001)

8 POPULATION GROWTH Source: ABS: 6416; REIA Increased one person per 80 seconds, largely by international migration Assume new housing demand at the rate of 3 persons per household, 133,000 new homes every year required to accommodate the new arrivals. Existing population, e.g., young Australians also looking to leave home or currently renting Thus, around 160,000 homes per annum needed.

9 Population growth for each of the states and territories is estimated by natural growth, net overseas migration, net interstate migration. On June 2014, people are attracted to NSW (26.2%), QLD (21.5%), VIC (32.2%), WA (13.1%)

10 HOUSING SUPPLY Limited government release of new land Government restrictions on the use of land preventing higher density land use Introduction by local councils of upfront infrastructure levies in the early 2000s. The average floor area of new houses increasing by up to 53.8% in the 18 years from to

11 LOW LENDING RATE Greater availability of credit due to financial deregulation Low interest rates from 2008 onwards (increasing borrowing capacity due to lower repayments) Cash rate decreased from 2.50 to 2.25 per cent on 4 Feb $29,750 is needed for a loan of $500,000 at 5.95% lending rate. $28,500 is needed when the rate reduced to 5.70%. Save $1,250 per year. Cheap finance, more people can afford to enter the market.

12 UNEMPLOYMENT RATE AND FAMILY INCOME The higher the income, the greater the affordability, the higher the demand for houses thus prices. On March 1994, the median weekly family income was $665 and $1,553 on March 2014, 1.34 times over 20 years.

13 FOREIGN INVESTMENT 2008 foreign investment rule changes for temporary visa holders. Foreign investment in Australian real estate had increased by more than 30% year to 2009 (FIRB, 2009) Properties are land bank sit vacant, not to rent them out. Several Australian Banks and lenders provide home loans to non-residents for purchasing of Australian real estate.

14 OTHER FACTORS A tax system that favours investors and existing home owners. Tax deduction and half of capital gain tax Investors using their superannuation have a tax advantage compared to 'savers' who are effectively taxed up to 70% on income from bank interest or bonds Confidence and expectation CPI remains at 3% average Unemployment rate 6% Low lending rate Shortage of dwelling supply Expectation on future price increases

15 EMPIRICAL STUDY UTS CRICOS PROVIDER CODE: 00099F UTS:DESIGN, ARCHITECTURE & BUILDING

16 A GENERAL FORM OF HOUSE PRICE MODEL A reduced-form equation for the price function derived on the supply and demand functions for owner-occupied housing (DiPasquale and Wheaton, 1994) Q d = f( x i, y i, z i, t) x i = macroeconomic variables such as GDP, interest rates, household income, and unemployment rate y i = housing-related variables such as unit transaction volume z i = demographic variables such as population, number of marriages, and birth rates. Q s = f(v i, t) v i = variables such as housing price, construction cost, and land supply. Q d = Q s >>>> P = f(q d, Q s, t) (t = 1, 2, 3, n)

17 DATA AND STEPS FOR ESTIMATION The estimated procedure for deriving the house price models includes: Collecting time series data which includes both supply and demand sides of the factors; Data analysis which consists of data pre-processing and correlation analysis; Selecting variables and developing multiple regression models using SPSS software and stepwise function; Verifying the developed models, analysing and interpreting the results.

18 DATA STATIONARY AND CORRELATION TESTS Unit root applying Augmented Dickey-Fuller test using Eview Correlation between the dependent and independent variables will also be tested. R-square, F-test, p-value Variance Inflation Factor (VIF) and DW were used to test the significance of the derived models.

19 RESULTS

20 CONCLUSION House price increases in Sydney during the last decade were determined by the factors of mortgage interest rate, housing supply and population growth Multiple regression analysis (MRA) is one of the methods applied to derive the main determinants. There are limitations using MRA. Vector Autoregressive (VAR) or nonlinear models is suggested for further study.

21 THANKS UTS CRICOS PROVIDER CODE: 00099F UTS:DESIGN, ARCHITECTURE & BUILDING