Spatial and Temporal Dynamics of the Singapore Housing Market Tay Jiajie, Darrell Department of Physics School of Physical and Mathematical Sciences 7 May 2014 Complexity Institute, Innovation Center 1
Have not returned to former highs 4 years Dow Jones Industrial Average Took about 5 years to return to its 2 years previous highs Picture Taken from Google Finance 2
Housing Markets • Many previous studies focus on liquid Inefficiencies Inefficiencies Slow to correct Slow to correct markets results in results in or exploit or exploit Deadweight Deadweight inefficiencies inefficiencies • Need to understand non-liquid markets such losses losses as the Housing market Leverage Leverage – Involves the livelihood of people Effects of crashes Effects of crashes – Dynamics (Time scales) of the two are different Prolonged Prolonged are amplified; are amplified; depression of depression of • Identify housing bubbles Market becomes Market becomes home prices home prices stagnant stagnant • Determine susceptibility to crashes • Measure effectiveness of policies 3
Overview and Previous Studies • Determinants of Home Prices – Supply and Demand – Income and Wealth • Equilibrium distributions – Wealth and Income Distributions – Housing Market Distributions • Time Series Analysis – Critical Transitions? 4
Theory of Wealth and Income • Pareto (1897) estimated that income distributions followed a power law • Mandelbrot (1960) proposed that only the tail end follows a power law • Other empirical evidence Klass, Oren S., et al. [2006] (Forbes 400): 1.49 Souma [2002] (High net worth Japanese): 2.05 5
Theory of Wealth and Income • Theoretical models done by Chakrabarti and Yakovenko – Exchanges in fraction of total wealth (E) – Exchanges with saving propensity (E-PL) – Additive/Multiplicative Processes (E-PL) • Exponential body and power law tail • Empirical Study of Yakovenko et al. showed similar features Draulescu, A. A., V. M. Yakovenko, 2001b, Physica A 299 , 213-221 6 Yakovenko, Victor M., and J. Barkley Rosser Jr. "Colloquium: Statistical mechanics of money, wealth, and income." Reviews of Modern Physics 81.4 (2009): 1703.
Home Price Distributions • Hedonic Model – Home price is a product of N factors, 𝑂 P = 𝐺 𝑗 𝑗=1 – The log-Price is a sum of random variables 𝑂 log 𝑄 = log 𝐺 𝑗 𝑗=1 – By the Generalized CLT, in the limit where 𝑂 → ∞ , log 𝑄 is normally distributed Ohnishi, Takaaki, et al. "On the evolution of the house price distribution." (2011). • Empirical studies showed that the tail is better fitted with a power law 7
Time Series Analysis • Treat bubbles a precursor to critical transitions • Length and time scale divergence • Spectral Reddening – Discrete Fourier transforms to analysis the Tan P. L. J., S. A. Cheong, Critical slowing down associated with regime shifts in the US housing market. Eur. Phys. J. B (2014) 87: 38 frequencies – Power concentrated in lower frequencies 𝜕 1/2 – Increased in autocorrelation and variances FT 8
Preliminary Works • Data on the Singapore Housing Market • Equilibrium Distributions and Deviations • Spatial and Temporal Dynamics of the Singapore Housing distributions • Time Series Analysis 9
Dwelling Types in Singapore Cost HDB Flats Private Properties Housing Condominium Landed Development (Condos) Properties Board (HDB) • Terraces • Condominium • 1-5 Room Flat • Semi Detached/ • Executive • Executive, HUDC Detached • Studio Apartments Condominiums (EC) • Bungalows Grants by HDB No grants No grants Highly regulated Foreigners allowed Singaporeans/PRs Singaporean/PRs to purchase 10
Private Sale Housing Data (1995-2012) • The Dataset 1. Address of Property 2. Price (Total, psf, psm) 3. Transaction Date 4. Type of Dwelling 5. District (Numbered) 6. Sectors (Colored) 11
HDB Housing Data (2000-2012) • The Dataset 1. Address of Property 2. Transaction Price 3. Floor Area 4. Transaction Month Unit Price is calculated Sectors chosen using address/town 12
Data Processing • Segregated into the different types – HDB Properties – Condominiums – Landed Properties – Sorted into Postal Districts/Sectors • Psf Price discounted using Historical CPI (Base Yr:2009) • Psf Data is fitted with: – Exponential – Pareto Distribution 13
Landed Properties Pareto Distributed Statistically significant power law with 𝛽 ≈ 5 Significance Testing using Clauset-Newman p-test ( 𝑞 = 0.058 ) 14
Results: Condominiums Fits well to an exponential distribution. (T = $444psf) Poorly fitted to a Pareto Dist. Empirical evidence that housing price is related to income Hump at the region $3k to $4.6k a Dragon King (DK). Appearance of Hump Hump No Hump Appears Persist Hump from 2007 and persists 15
Possible Explanation ‘Investment’ class districts (D9, 10) contributed to the hump Upper Quartile Price Movement generally in tandem followed by wild swing in 2006-07 Deviation of Prime, Investment Greatly affected by Grade Properties Price as start 2008 correction of bubble formation 16
Spatial and Temporal Dynamics Prices starts to increase during 2006 in agreement with the stationary analysis The bubble starts in District 9 and 10 and spreads out radially 17
Sliding Window 1 month Time Series Analysis • DFT to detect Spectral Reddening • Autocorrelation • Sliding 2 years window • Every slide = 1 month 18
Discrete Fourier Transform Power evenly spread out during quiet years Power concentrated at lower frequencies during possible bubble years Asian Financial Crisis 19
Lag1 Autocorrelation Autocorrelation low at quiet years Autocorrelation spikes up during the possible bubble years Asian Financial Crisis can also be seen 20
HDB Properties Agree with Income/Wealth Exponential Distributed with crossover at $600psf Second regime appeared only after 2009 Price in the different districts move in tandem 21
Comparison across types • Housing distribution emulates Income/Wealth distribution • Lead Lag Relationship Exponential Pareto • Bubble in Condominiums, but not in Landed and HDB • Bubble spreading spatially, but not across housing types 22
Future Works • Comparison to Taiwanese Data – Not as highly segregated as Singapore – Exponential body and power law tail – The segregation should be in locations • Expect to pick out Universal features – Discover non-equilibrium features – Emulate income/wealth distribution 23
Future Works and Summary • Build Agent Based Models – Expand on the computation models proposed by Chakrabarti and Yakovenko – Develop a model for housing that can be used for scenario testing – Bubble spreading across the different housing type 24
Future Works and Summary • 8 cooling measures from 2009 to 2013 • Critical slowing down in Singapore • Round 1 (14 September 2009) Housing Market • Round 2 (20 February 2010) – News reports in Singapore suggesting that • Round 3 (30 August 2010) cooling measures are working • Round 4 (14 January 2011) – We determine that stability of the housing • Round 5 market (7 December 2011) – Treat cooling measures as perturbations • Round 6 (5 October 2012) – System is stable if recovery rates are • Round 7 (11 January 2013) increasing and converging • Round 8 (28 June 2013) 25
References 1. A. Dragulescu and V. M. Yakovenko, Physica A: Statistical Mechanics and its Applications 299, 213 (2001). V. Pareto, Cours d’Economie Politique, Lausanne, 1897 2. 3. V. M. Yakovenko and , J. Barkley Rosser, Jr., REVIEWS OF MODERN PHYSICS, VOLUME 81, OCTOBER – DECEMBER 2009 4. B. Mandelbrot, Int. Econom. Rev. 1 (1960) 79 5. Klass, Oren S., et al. Economics Letters 90.2 (2006): 290-295. 6. Souma, Wataru. Springer Japan, 2002. 343-352. 7. SingStats, 2013 8. Urban Redevelopment Authourity, 2013 9. Tan, James Peng Lung, and Siew Ann Cheong. "Critical slowing down associated with regime shifts in the US housing market." The European Physical Journal B 87.2 (2014): 1-10. 26
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