Spatial and Temporal Dynamics of the Singapore Housing Market Tay - - PowerPoint PPT Presentation

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

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2

Have not returned to former highs

Dow Jones Industrial Average

Took about 5 years to return to its previous highs

Picture Taken from Google Finance

4 years 2 years

Housing Markets

3

Slow to correct

• r exploit

inefficiencies Inefficiencies results in Deadweight losses Effects of crashes are amplified; Market becomes stagnant

• Many previous studies focus on liquid

markets

• Need to understand non-liquid markets such

as the Housing market

– Involves the livelihood of people – Dynamics (Time scales) of the two are different Leverage Prolonged depression of home prices Slow to correct

• r exploit

inefficiencies Inefficiencies results in Deadweight losses Effects of crashes are amplified; Market becomes stagnant Leverage Prolonged depression of home prices

• Identify housing bubbles
• Determine susceptibility to crashes
• Measure effectiveness of policies

Overview and Previous Studies

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• Determinants of Home Prices

– Supply and Demand – Income and Wealth

• Equilibrium distributions

– Wealth and Income Distributions – Housing Market Distributions

• Time Series Analysis

– Critical Transitions?

Theory of Wealth and Income

• Pareto (1897) estimated that income

distributions followed a power law

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• 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

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

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Draulescu, A. A., V. M. Yakovenko, 2001b, Physica A 299, 213-221

Yakovenko, Victor M., and J. Barkley Rosser Jr. "Colloquium: Statistical mechanics

• f 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, P = 𝐺𝑗

𝑂 𝑗=1

– The log-Price is a sum of random variables log 𝑄 = log 𝐺𝑗

𝑂 𝑗=1

– By the Generalized CLT, in the limit where 𝑂 → ∞, log 𝑄 is normally distributed

• Empirical studies showed that the tail is

better fitted with a power law

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Ohnishi, Takaaki, et al. "On the evolution of the house price distribution." (2011).

Time Series Analysis

• Treat bubbles a precursor to critical

transitions

• Length and time scale divergence
• Spectral Reddening

– Discrete Fourier transforms to analysis the frequencies – Power concentrated in lower frequencies – Increased in autocorrelation and variances

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FT 𝜕1/2

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

Preliminary Works

• Data on the Singapore Housing Market
• Equilibrium Distributions and Deviations
• Spatial and Temporal Dynamics of the

Singapore Housing distributions

• Time Series Analysis

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Dwelling Types in Singapore

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Housing Development Board (HDB) Condominium (Condos) Landed Properties

• 1-5 Room Flat
• Executive, HUDC
• Studio Apartments
• Condominium
• Executive

Condominiums (EC) Grants by HDB Highly regulated Singaporean/PRs No grants Foreigners allowed to purchase

• Terraces
• Semi Detached/

Detached

• Bungalows

No grants Singaporeans/PRs Cost HDB Flats Private Properties

Private Sale Housing Data (1995-2012)

• The Dataset

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• 1. Address of Property
• 2. Price (Total, psf, psm)
• 3. Transaction Date
• 4. Type of Dwelling
• 5. District (Numbered)
• 6. Sectors (Colored)

HDB Housing Data (2000-2012)

• The Dataset

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• 1. Address of Property
• 2. Transaction Price
• 3. Floor Area
• 4. Transaction Month

Unit Price is calculated Sectors chosen using address/town

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

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Landed Properties

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Statistically significant power law with 𝛽 ≈ 5 Significance Testing using Clauset-Newman p-test (𝑞 = 0.058) Pareto Distributed

Results: Condominiums

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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 from 2007 and persists

No Hump Hump Appears Hump Persist

Possible Explanation

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‘Investment’ class districts (D9, 10) contributed to the hump Upper Quartile Price Movement generally in tandem followed by wild swing in 2006-07 Greatly affected by 2008 correction Deviation of Prime, Investment Grade Properties Price as start

• f bubble formation

Spatial and Temporal Dynamics

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Prices starts to increase during 2006 in agreement with the stationary analysis The bubble starts in District 9 and 10 and spreads out radially

Time Series Analysis

• DFT to detect Spectral Reddening
• Autocorrelation
• Sliding 2 years window
• Every slide = 1 month

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1 month Sliding Window

Discrete Fourier Transform

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Power evenly spread out during quiet years Power concentrated at lower frequencies during possible bubble years Asian Financial Crisis

Lag1 Autocorrelation

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Autocorrelation low at quiet years Autocorrelation spikes up during the possible bubble years Asian Financial Crisis can also be seen

HDB Properties

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Agree with Income/Wealth Exponential Distributed with crossover at $600psf Second regime appeared

• nly after 2009

Price in the different districts move in tandem

Comparison across types

• Housing distribution emulates

Income/Wealth distribution

• Lead Lag Relationship
• Bubble in Condominiums, but not in

Landed and HDB

• Bubble spreading spatially, but not

across housing types

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Pareto Exponential

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

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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

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Future Works and Summary

• 8 cooling measures from 2009 to 2013
• Critical slowing down in Singapore

Housing Market

– News reports in Singapore suggesting that cooling measures are working – We determine that stability of the housing market – Treat cooling measures as perturbations – System is stable if recovery rates are increasing and converging

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• Round 1

(14 September 2009)

• Round 2

(20 February 2010)

• Round 3

(30 August 2010)

• Round 4

(14 January 2011)

• Round 5

(7 December 2011)

• Round 6

(5 October 2012)

• Round 7

(11 January 2013)

• Round 8

(28 June 2013)

References

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1.

• A. Dragulescu and V. M. Yakovenko, Physica A: Statistical Mechanics and

its Applications 299, 213 (2001). 2.

• V. Pareto, Cours d’Economie Politique, Lausanne, 1897

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.