residential forward vs spot market empirical analysis of property

Segregated Funds Group

Residential “Forward” Vs “Spot” Market :

Empirical Analysis of Property Prices &

Investment Selection

(A Case Study of Gurgaon, India)

In Collaboration with:

Department of Financial Studies,

South Campus, Delhi University &

NCFMR

JLL SFG Technical Research Paper 1 / Sep 2014

Agenda

I. Introduction

a) Defining Spot and Forward Residential Real Estate Market

b) Research Background

II. Research & Analysis

a) Defining Key research questions

b) Key results and empirical analysis

III. Key Findings Summary

IV. Predictive Analytics

V. Annexures

1

Recently….

2

3

News & Data

Does Data create News? Can we Predict the News using Data?

4

Data “..Data is no longer regarded as static… data is becoming the raw material of business.. Used to create economic value, innovation and new Services and can reveal secrets to those with the tools to listen..”

•Viktor Mayer Schonberger & Kenneth Cukier in their book “Big Data”

“Small Data” Correlation:

Comparable” & Micro

market based data

analysis approach

Causality & Correlation:

Sampling & Statistical

Hypothesis based data

analysis.

“Big Data” Probabilistic

model

Probabilistic & Aggregate

Data based approach

Future Past Present

Data Analytics & Real Estate Investment Management

Data Analysis is key to “underwriting” assumptions, Investment selection, asset

management and driving overall risk adjusted returns

Data Analytics key to

Investment management

in the future

6

I. Introduction

Introduction

a) Defining Spot &

Forward Residential

Real Estate Market

b) Research Background

I. Introduction

7

Forward and Spot Market Definition

FORWARD MARKET

SPOT MARKET

Time = t0 Time – t1

1st Quarter

2nd Quarter in 5 years (20 quarters)

Booking amount =

10% (Buyers equity)

5% installment

(Buyers equity)

Buyers equity = 20%

Mortgage Financing (LTV) at t0 = 80%

Monthly EMI based on 20 year loan

We Define Under Construction as “Forward” and Completed as “Spot”. The Forward is not a

direct “derivative” of the spot as the underlying asset (residential unit) is also being created.

However in 5 years a Forward asset becomes a Spot

Under construction

Completed

land

5% installment

(Buyers equity)

Mortgage Financing (LTV) = 80% at time t3 (after buyer has

paid 20% equity) Monthly EMI based on 20 year loan

The Starting Point…

8

#1 Residential “Prices”: Data based on “Price” of transaction without “Attribute” analysis

#2 Market “Separation” View : We view Forward Market as an indirect “derivative” of the Spot market. A Long run relationship needs to be identified

#3 Market “Causality”: Data focusses on “What” and not “Why”. Information flow between Spot and Forward needs to be analysed

#4 Perception Vs Pricing: Most studies focus on Pricing leading to Perception. The opposite may be true.

#5 Investment Selection & Returns: Can Risk Adjusted Returns Probability aid

Investment Selection?

Research Background - Gurgaon Micro Market Analysis

9

Gurgaon Micro-markets

3 Micro markets

1. Golf Course Road,

2. Golf Course Extn. and

3. Sohna Road

97 projects were

evaluated

1. 37 (Completed) Spot

2. 60 (Under construction)

Forward

Period of Study: Q1

2008 to Q4 2013

The projects were

detailed out in terms of:

1. Pricing;

2. Number of units;

3. Qualitative attributes;

4. Forward Projects became

Spot 5 years from launch

10

II. Research & Analysis

Research & Analysis

a) Defining Key Research

Questions

b) Key results and empirical

analysis with methodology

I. Perception Survey

II. Data Stationarity &

Statistical Tests

III. Long Run Relationship

between Forward and

Spot Markets

IV. Market Value of

Invested Equity (Spot

Vs Forward)

V. Risk Adjusted Return

Analysis

VI. Quality Premiums

Analysis

Key Research Questions (Forward vs Spot)

Attributes &

Investment

Selection

1 Risk

Adjusted

Returns

4 Forward vs

Spot - Long

Run

Relationship

2 Market Value

of Invested

Equity

Intra Market

Premium

3 5 What factors

do buyers

evaluate in

residential

property?

How are Spot

and Forward

market

related?

How does

value of

investment

vary in Spot

and Forward?

How does risk

adjusted

return vary in

Spot and

Forward?

Do buyers

price

differentiated

quality?

11


Attributes &

Investment

Selection

1 Q1) Attributes & Perception

What are Key Attributes defining investment selection?

What are Relative Weightage of Attributes assigned by buyers?

Methodology

Detailed Primary & Secondary Survey carried out.

5 Broad Attributes agglomerated from 29 sub-parameters

97 Projects ranked against all sub-parameters

Weightage assigned to each parameter

Composite Score on a scale of 1 to 10 identified for each

project

Projects Classified into A, B+, B- and C category on basis of

composite score

Cronbach Alpha : 0.816

indicating internal reliability

of scale of survey. We

received 82 detailed

responses.

12

Key Results & Analysis

Attributes &

Investment

Selection

1 The properties were divided into homogeneous groups

separately for Spot and Forward according to their

composite scores.

Range of composite score of for Spot: 5.67 to 8.45.

Range of composite score for Forward : 4.67 to 9.17

Division of homogeneous groups

(for both Spot and Forward) :

• Above 7.5 : Category A

• 6.75 – 7.5 : Category B+

• 6 to 6.75 : Category B-

• Below 6 : Category C

Composite score =

Rank of property for the parameter * Weightage of particular parameter

13


1 Forward vs

Spot Long

Run

Relationship

2 Q2) How are Forward and Spot Markets related

Are these Markets Correlated or Co Integrated

Testing for which market leads and which follows

Testing for Causality and direction of information flow

What is the long term association between

macroeconomic variables and Spot/Forward prices?

Methodology

Weighted Mean Prices tabulated for projects in category

Augmented Dickey Fuller Test: Data Stationarity

Johanson Co-integration Test: Market Co integration

Vector Error Correction Model (VECM) Test: Spot

Market dominant Forward follows

Granger’s Causality Test: Information flows from Spot

to Forward.

14


1 Forward vs

Spot Long

Run

Relationship

2

Results

ADF test Conclusion:

Data was stationary for First / Second difference of prices for all

categories. This implies the mean and variance of the data will be

the stable over time for the Spot and Forward market. However at

the price level the data was non stationary

Johanson Co-integration test Conclusion:

Spot and Forward market share common long run information

for each category (A, B+, B-, C) and there is a price discovery

process. Therefore, showing informational efficiency between the 2

markets.

VECM test Conclusion:

The spot markets leads the price discovery process and the

forward market follows it for all classes (A, B+, B-,C) In the event

of deviation from equilibrium in the short run, it is the forward market

that makes a greater adjustment than the spot market in order to

restore the equilibrium.

Granger’s Causality Test Conclusion:

It is confirmed that Spot market is causing Forward market.

Spot and Forward Markets

are Co-integrated with Spot

Leading the Forward and

information flowing from

Spot to Forward

15

Relationship between macroeconomic variables and Spot /Forward prices Forward vs

Spot Long

Run

Relationship

2


16

Spot Market

Category GDP

Rupee / $

Exchange

Rate

Inflation Bank

credit

Home loan

interest

rates

Stock

Market

(NIFTY)

Time period for Property prices to follow

macro-economic variables

A × × × GDP - 4 quarters ; Bank Credit - 1 quarter

B+ × × × × -

B- × × GDP - 1 quarter; Bank Credit - 1 quarter

C × × × GDP - 1 quarter; Bank Credit - 1 quarter

Forward Market

Category GDP

Rupee / $

Exchange

Rate

Inflation Bank

credit

Home loan

interest

rates

Stock

Market

(NIFTY)

Time period for Property prices to follow

macro-economic variables

A × × × GDP - 4 quarters ; Exchange Rate - 4

quarters

B+ × × × × GDP -3 quarters ; Bank credit - 3 quarters

B- × × Bank Credit - 4 quarters

C × × × GDP - 2 quarters ; Bank Credit - 2 quarters

India’s

GDP

Currency

Movements

India’s

Inflation

(WPI)

India’s total

non-food

bank credit

India Home

loan Interest

Rates

Stock

Market

(NIFTY)

Valid relationship between

macro-economic variable

and Spot/Forward

Spurious relationship

between macro-economic

variable and Spot/Forward

× No relation between

macro-economic variable

and Spot/Forward


Market Value

of Invested

Equity

3 Q3) How does Market Value of Invested equity vary

What is the general trend of invested equity in Forward

and Spot markets independently

What is the long run equilibrium relationship of invested

equity in Forward and Spot within each category

Invested Equity in Spot market at time t :

σspot t = Pspot 0 * [X0 + i*∑t t=0(1- X0 )] + (Pspot t - Pspot 0 )

Where,

• (Pspot 0*X0 ) : upfront down payment (at time t=0);

• Pspot 0 [i*∑t

t=0(1- X0 )] : invested debt

Both these two factors combined to form the overall equity invested factor

• (Pspot t - Pspot ) : market premium

Invested Equity in Forward market at time t :

σfwd t = Pfwd 0 * ∑2

t=0Xfwd t + Pfwd 0 * i3* ∑tt=3Xfwd t + (Pfwd t - Pfwd 0)

Where,

• (Pfwd 0 * ∑2

t=0Xfwd t) : upfront down payment (at time t=0, 1, 2)

• (Pfwd 0 * i3* ∑tt=3Xfwd t) : invested debt (from time t=3)

• (Pfwd t - Pfwd 0) : market premium. Assumptions • Property Price @ time 0/launch = Pspot 0 Pfwd 0

• Property Price @ time t = Pspot t Pfwd t

• Equity invested at time t=0:

Fwd: Xfwd 0* Pfwd 0 where Xfwd 0 = 10%

Spot: X0* Pspot 0where X0 = (1-LTV) = 20%


Xfwd 1* Pfwd 0 where Xfwd 1 = 5%


Xfwd 2* Pfwd 0 where Xfwd 2 = 5%

• Interest rate at t=3 is i3

17


Market Value

of Invested

Equity

3 Trend line is downward

sloping. Rate of change of

prices in Spot is higher than

that of Forward

y = -0.0166x + 0.895

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

Ratio of forward / spot mkt. value of invested equity (Level B+)

Fb+/Sb+

Trend line is downward

sloping; with negative slope of

trend line being the highest,

rate of change of prices in Spot

is higher than the rate at which

Forward prices change,

compared to Level A

Level A Properties

Level B+ Properties

18

y = -0.0124x + 0.9103

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

Ratio of forward / spot mkt value of invested equity (Level A)

Fa/Sa


3 y = -0.002x + 0.9233

0.00

0.50

1.00

1.50

2.00

Ratio of forward / spot mkt. value of invested equity (Level B-)

Fb-/Sb-

y = 0.0059x + 0.7045

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Ratio of forward /spot mkt. value of invested equity (Level C)

Fc/Sc

Market Value

of Invested

Equity

3 Trend line flat. The rate at

which prices of the Spot

change will be almost equal

compared to the rate at which

Forward changes

Trend line is upward sloping.

The rate of change of prices in

the Spot market will be lower

compared to the rate of

change of prices in Forward

market

Level B- Properties

Level C Properties

19


Risk

Adjusted

Returns

Q4) Which Category Project has higher Risk Adjusted

Returns?

What is the general trend of Risk Adjusted Returns in

Forward and Spot markets independently

How do risk adjusted returns vary in Forward and Spot

within each category

Sharpe Ratio is calculated as:

(Rp – Rf) / σ

• Rp : return on a each class

• Rf : risk-free rate (91 day T

bill rate)

• σ : standard deviation of

excess returns

Methodology

The Sharpe Ratio is calculated for each return series for

each class (A, B+, B-, C) in the Spot and Forward

market by dividing the excess returns for each class by

the average standard deviation of these excess returns

For calculating the ratio, the implicit yield on 91-day

Treasury Bills (annualized implicit yield based on daily

average rate was divided by 4 to obtain the average

quarterly rate for the 91-day T-Bills.

20

4


Risk

Adjusted

Returns

Results

Conclusion:

In the Spot market, higher quality projects (B+) perform

better

while in the Forward market, the lowest quality projects

perform better in terms of risk adjusted returns.

Sa Sb+ Sb- Sc Fa Fb+ Fb- Fc

Average of excess

returns 0.0218 0.0255 0.0179 0.0145 0.0161 0.0063 0.0133 0.0124

Standard Deviation

of Excess Returns 0.0844 0.0575 0.0479 0.0519 0.0673 0.0406 0.0591 0.0370

Sharpe Ratio 0.2588 0.4433 0.3733 0.2785 0.2395 0.1555 0.2260 0.3348

B+ properties give

highest return in

Spot market

C properties give

highest return in

Forward market

21

4

Key Research Questions (Spot vs Forward)

Intra Market

Premium

5 Q5) What has been the Impact of Perception on Pricing

What are buyers willing to pay from moving from a lower

category to higher category in Forward and Spot Market

Independently.

Do buyers discount or compound price commensurate to

perception quality?

Methodology

Each property in our data has been given a composite

score according to their quality and goodwill.

Weighted Mean Prices tabulated for projects in category

We analysed the trends in the extra price premium

buyers pay for moving from a lower category to a higher

category residential property.

22


Intra Market

Premium

5 3392

293

3966

-65

-1000

0

1000

2000

3000

4000

5000

Spot Market Quality Premium

Moving from Sb+ to Sa Moving from Sb- to Sb+ Moving from Sc to Sb-

-276

3461

1979 1562

-1000

0

1000

2000

3000

4000

Forward Market Quality Premium

Moving from Fb+ to Fa Moving from Fb- to Fb+ Moving from Fc to Fb-

Higher Premium for slight

improvement (B- to B+) in

perception in both Markets.

Category A has been

heavily Price discounted

by buyers for a short while

23

III. Key Findings Summary

Key Findings Summary

Attributes &

Investment

selection?

1 Risk

Adjusted

Returns

4 Fwd. vs Spot

Long Run

Relationship

2 Market Value

of Invested

Equity

Intra Market

Premium

3 5 1. Developers Goodwill (Branding) perception is a high contributor to Investment Selection in Forward Market

2. Spot and Forward Markets are Co-integrated with Spot Leading the Forward and information flowing from

Spot to Forward

3. The Slope of The Forward to Spot Ratio is lowest in Category B+ and highest (upward trending) in Category C

4. In the Spot market, higher quality projects (B+) perform better, while in the Forward market, the lowest

quality projects perform better in terms of risk adjusted returns.

5. Higher Premium for slight improvement (B- to B+) in perception in both Markets. Category A has been

heavily Price discounted by buyers for a short while

25

IV. Predictive Analytics Predictive Analytics

a) Building Blocks of the

model

Predictive Model

Predictive Analytics based on

Information flow from Spot

Relationship with Spot

Investment return

benchmarks

Information flow to Forward

Relationship with Forward

Investment Return

benchmarks

Perception analysis of attributes

LTV, interest rates and Macro

economic factors

Price as per category

Perception analysis of attributes

Interest rates & Macro economic

factors

Price as per category

Spot Forward

Building Blocks of the Model

Disclaimers JLLIA

28

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herein are indicative only and may vary significantly from prices available from other sources. An indicative price of a transaction/security/instrument may differ substantially from an

actionable value. Indicative price and availability are subject to change without notice.

As a general report, this document represents the views of the Authors in relation to Indian economy and the real estate industry. The information is based on projections, opinions,

forecasts and material that are believed to be reliable. Market data and certain industry forecasts used through the presentation have been obtained from market research, publicly

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advice, or opinion regarding the appropriateness of any investment. This research is for educational / informational purposes only and should not be relied for making any investments.

About Segregated Funds Group, (Jones Lang LaSalle Investment Advisors)

The Segregated Funds Group (SFG) is a real estate private equity investment management entity focused towards directing investments in the Indian real estate market. Launched in

2012 by Jones Lang LaSalle, one of the world’s leading financial & professional services firm specializing in Real Estate, the Segregated Funds Group is a natural extension of Jones

Lang LaSalle’s presence in India, and is based on the firm’s vast experience in the funds management business globally. Based out of Delhi NCR, the specialized investment

management firm comprises a team which has strong investment and development experience across India.

For further information, please visit www.jllsfg.com

Authors & Acknowledgement

Mridul Upreti

Chief Executive Officer

JLL Segregated Funds Group India

[email protected]

Dr. Sanjay Sehgal

Professor


Delhi University

[email protected]

Aakriti Bhatia

Analyst


[email protected]

Piyush Pandey

Research Fellow


Delhi University

[email protected]

29

We would like to acknowledge the continuous assistance and

advice of JLL SFG team, The Department of Financial

Studies, Delhi University and NCFMR (National Council of

Financial Market Research) for their contribution towards this

project. We acknowledge Sonia Kumari, Summer Intern, JLL

SFG who contributed to our study in the initial stages. We

would like to gratefully acknowledge the assistance of JLL

who participated in our surveys and actively contributed to

constant inputs on real estate. We would also like to thank the

senior management and subject matter experts of JLL who

gave us their opinions on each stage of our research paper.

We also thank two anonymous reviewers who reviewed the

technical paper. We welcome any feedback on the published

results to continue to improve future editions and make them

as meaningful as possible for our readers.

If you are interested in reading our research paper in more

detail, you can contact us at www.jllsfg.com

For further details, Please Contact

Isha Kapila

Marketing & Communications


[email protected]

Study Collaborated by:

Segregated Funds Group

Thank you

JLL SFG Technical Research Paper 1 / Sep 2014

Annexures - Assumption set and Exclusions

All project names, number of units of each property, prices of properties and status of property (under-construction and

completed) has been taken from JLL REIS data from a period of Q1 2008 to Q4 2013.

97 properties identified are a sample of the Gurgaon market; not the entire Gurgaon market. A sample has been taken by

identifying properties in 3 major micro-markets of Gurgaon.

All Projects which have been Proposed by the Developer (construction not yet started), have been categorized as Under-

Construction.

Only apartments have been Apartments for research paper. Villas and plots have not been included in it to make

information homogeneous. Out of a total of 147 residential units in our 3 micro-markets, 97 were apartments.

The quarterly housing loan interest rates used for the purpose of equity and IRR calculation have been taken from State

Bank of India (SBI).

We have assumed Floating rates for the duration 5 years to 15 years and for an amount more than 50 lakhs. This is

because most properties in Gurgaon come within this price range

Properties in which only a few towers are completed and possession if given in those towers, while few towers may still

be under-construction have been considered to be in the Spot market (completed)

To calculate the density for each property, we have multiplied the number of acres with the number of persons in a family

and divided it by the number of units. We have assumed 5 persons to be there in each family

All results of the Forward market are based on a construction linked payment plan where the investor pays 10% as

booking amount, 5% in the first quarter, 5% in the second quarter.

In the forward market, projected has been assumed to be completed in 5 years.

All results of the Spot market are based on the assumptions that Loan to value ratio is 80:20; where the investor will pay

20% as booking amount and the rest as a loan from the bank.

20 year period of loan has been assumed in our research paper.

31

32

Annexures – Attribute Analysis

BROAD FACTORS SUB-FACTORS

Developers goodwill

People Perception of Developer Goodwill

Number of years in industry

Number of square feet built

Private Equity Participation

Location and Accessibility

Close to airport

Close to highway(NH-8)

Close to School

Close to hospital

Close to office

Close to metro station

Close to bank

Close to shopping complex

Amenities and Facilities

Security System

Garden area and open spaces

Centrally air conditioned

Club-house and sports facilities

Fire safety system

Parking space

100% power back up

Round the clock water availability

Earthquake resistant

Housing complex away from main road

Convenience store in complex

Electricity cost/Power back-up cost

Other maintenance charges

Density Low density of residential complex(less number of persons per acre)

Less number of residential units per floor

Construction Quality Quality of construction materials/fixtures/flooring

Quality of plastering on walls

5 broad parameters and

29 sub-parameters were

identified.

These are the factors

that an individual looks

at while investing in a

residential property.

Step 1 – Identification of

parameters

Source:

• Series of meetings with JLL subject

matter experts, brokers, developers

and investors

• Reviewing of various property

brochures and property websites

33

Step 2 – Preparation of

Rank Sheet for properties


Ranking for Developer’s Goodwill

Developer’s goodwill is categorized under 4 categories and each developer has been ranked out of 10

Number of years in

industry

Benchmark ranking:

• 0-10 years : 2

• 11- 25 years : 5

• 26-50 years: 8

• Above 50 years: 10

Number of square feet

built:

Benchmark ranking:

• 0 sqft : 0

• 1 – 10 million sqft : 2



• Above 100 million sqft : 10

Private Equity

Participation:

Benchmark ranking:

• If “yes” : 10

• If “no” : 0

• If “yes” but not doing well:5

Person perception of

Developer Goodwill

A Survey was conducted to gauge

the personal perception of an

individual about a developer.

All developers were divided and

placed into 3 categories:

i. Only Spot; ii. Only Forward;

iii. Both Spot and Forward Source:

• Visiting developer

websites; calling the

developer’s head office

Source:

• Visiting the developer

website; calling the

developer’s head office;

calculating the total area

under-construction and

completed by the developer

Source:

• Meetings with JLL subject

matter experts; visiting

developer websites A total of 8 responses were

collected where each person had

to rank each developer on a scale

of A to E

(A: Excellent; B:Very Good; C:

Average; D: Poor; E: Very Poor)

Following was the benchmark for

ranking:

A – 10

B – 8

C – 6

D – 4

E – 2

Note (for benchmark ranking for all sub-parameters)

If the benchmark rank is red, an absolute rank that is specified is given.

If the benchmark rank is in black, a cumulative rank is given by adding the respective factors rank.

34


Step 2 (cont.)– Preparation

of Rank Sheet for properties Ranking for Amenities and Facilities

Amenities and Facilities is categorized under 13 categories. Following is the benchmark ranking:

BROAD FACTOR SUB-FACTOR

Security

System

No provision for security 0

Perimeter Security – BASIC 2

Security Guards 1

Gated Community 1

Boom Barriers 1

Alarm system 0.5

Smart card access/ Intelligence access control

system 1

CCTVs 1

Video-phone access control system 1

Panic alarm button in bedroom 0.5

Intercoms linking main-gate and apartment 1

Garden area

and open

spaces

No garden area 0

Green landscaping / gardens – BASIC 5

Natural sunlight / Naturally lit-up 1

Ventilators / airy apartment 1

Water bodies in complex 1

Quiet / peaceful / tranqulity / Vaastu compliant 1

Percentage of greenery ( more than 50%

greenery) 1

BROAD FACTOR SUB FACTOR

Centrally air

conditioned

No air-conditioning 0

ACs only in bedrooms/dining - BASIC 3

Split AC in bedrooms/dining 5

Centrally AC apartment 7

Centrally air-conditioning with VRV/VRF

technology 9

Centrally air-conditioning with ACs in apartments

and lobbies 10

Club-house and

sports facilities

No clubhouse facilities / Case in court 0

Provision of gym, basket-ball, tennis, swimming

pool, banquet hall, jogging track, squash court –

BASIC

5

Provision of indoor games card rooms, reading

rooms, yoga room, billiards, video-games,

aerobics/dance room, table tennis, bowling alley

1

Restaurant 0.5

Home- theatre 0.5

Indoor heated pool / 2 swimming pools in complex

/ Olympic size pool 0.5

Spa, Steam, Sauna, Jacuzzi 0.5

Parlour / Salon 0.5

Golf course range/ Golf Driving range/ Virtual golf

provision in complex 0.5

Creche 0.5

Doctor-on-call, Concierge-on-call, Automatic car

wash 0.5

35


Step 2 (cont.)– Preparation

of Rank Sheet for properties

BROADFACTOR SUB-FACTOR

Fire safety system

No fire fighting system 0

Complete fire fighting system in

place (as per basic norms) -

BASIC

6

Fire detection and sprinkler

system 2

Smoke detectors 1

Parking space

No parking space 0

Ample parking space (No

dedicated / reserved parking for

each apartment) - BASIC

4

Dedicated parking for each

apartment 6

Basement Parking 7

2-level basement parking 8

3- level basement parking 10

100% power back up

No power back up 0

100% power back up – BASIC 7

Below 7.5 KVA 1

KVA – 7.5 KVA to 15 KVA per

apartment 1

KVA – Above 15 KVA 1

BROADFACTOR SUB-FACTOR

Round the clock water

availability

24 hours provision of water –

BASIC 5

Rain water harvesting 1

Supply of geysers / Provision of hot

water in taps and basins 1

Water sewage treatment plant /

Treatment / recycling of water

supply/ water softening

1

Solar heating systems 1

Provision for water purification (

RO system / Membrane

technology)

1

Earthquake resistant

Not earthquake resistant 0

Earthquake resistant building 8

Building designed as per Zone 5

norms 2

Housing complex away

from main road

Yes 4

No 6

Convenience store in

complex

Yes 10

No 0

Electricity cost/Power

back-up cost • - 0

Other maintenance

charges Rs. 2.5 per unit 0

Amenities and Facilities is categorized under 13 categories.

Following is the benchmark ranking:

Source: Property Brochures; Property websites; Calling head

office of developer; Meetings with subject matter experts

36

Annexures – Primary Survey

Step 3 – Computation of

weightages

A detailed questionnaire was prepared to

compute the weight of the sub-factors.

37


Step 2 (cont.)– Preparation of

Rank Sheet for properties

38


39

Annexures – Result of Attribute Analysis

Step 3 (cont.) – Computation

of Weightages

Results of the weightages computed for each

parameter

Broad Factor Sub-Parameter Completed Under-

construction

Developers Goodwill

Developers Goodwill 23.00 31

Location and Accessibility

Close to airport 2.10 2.16

Close to highway 2.48 2.57

Close to school 3.03 2.97

Close to hospital 3.04 3.06

Close to office 2.95 3.01

Close to metro station 2.90 2.91

Close to bank 2.45 2.43

Close to shopping complex 3.06 2.88

Density Low density 7.56 5.62

No. of residential units per

floor 7.44 5.38

Construction

Quality

Flooring / Provision of interior

fittings / Quality of construction 9.40 8.88

Quality of plastering on walls 9.20 8.72

Broad Factor Sub-Parameter Completed Under-

construction

Amenities

and

Facilities

Security system 1.72 1.48

Garden area and open

spaces 1.78 1.53

Centrally AC 1.13 0.98

Clubhouse and Sports

facilities 1.53 1.34

Fire safety system 1.70 1.50

Parking Space 1.83 1.55

Power back up 1.82 1.58

Round the clock water

availability 1.90 1.60

Earthquake resistant 1.74 1.51

Housing complex away

from main road 1.41 1.24

Convenience store in

complex 1.65 1.37

Electricity/Power back-

up cost 1.62 1.39

Maintenance charges 1.57 1.34

40

Annexures – Empirical Analysis

STEP 1: Testing the Stationarity of data

Augmented Dickey Fuller Test

• We want to test whether the Spot and Forward market are co-integrated. However, the concept of

co-integration becomes relevant only when the data is non-stationary

• This testing for stationarity was done through the Augmented Dickey Fuller (ADF) test.

• ADF tests the presence of unit root further to which the series is said to be non stationary.

The testing procedure is to apply to the following model:

∆yt = α + βt + γ yt - 1 + δ1∆ yt - 1 + …….δt-1 ∆ yt – p + 1 + εt

where

• α : constant;

• β : coefficient on a time trend and

• P : the lag order of the autoregressive process.

• T : time

• y : dependent variable

41

Level First Difference Second Difference

t- statistics

(p-value)

t- statistics

(p-value)

t- statistics

(p-value)

Inference on

integration

Sa -1.90

(.621)

-5.05

(.005)* - 1

Sb+ -1.32

(.857)

-3.58

(.059)** - 1

Sb- -1.89

(.627)

-2.68

(.254)

-5.26

(.002)* 2

Sc -4.61

(.007)* - - 0

Fa -3.20

(.110)

-3.97

(.028)* - 1

Fb+ -1.82

(.664)

-3.83

(.034)* - 1

Fb- -1.52

(.785)

-3.90

(.033)* - 1

Fc -3.35

(.0857)

-1.54

(.782)

-4.35

(.017)* 2

Note: • Figures in brackets () indicate the p-values;

• denotes significance at 5% level.

• ** denotes significance at 10% level

Results of Augmented Dickey Fuller (Unit Root) Test


42

STEP 2: Testing the stable long run relationship between the Spot and

Forward Mean Prices Johansson Co-integration Test

The current forward (or spot) price could be represented as being dependent on the spot (or forward) price, as under:

Ft = α1 + β1 St + ϵ1t (1)

or

St = α2 + β2 Ft + ϵ2t, (2)

• Ft and St : forward and the spot prices at time t.

The above can be re-written with residuals, as under:

Ft – α1 - β1St = ê1t (3)

or

St - α2 – β2 Ft =ê2t (4)

where

• êt : white noise residual term.

Equations (3) and (4) are linear combinations of Ft and St. If either ê1t or ê2t is stationary, then one of them is I(0)

and there is at least one long run relationship between Ft and St


43

Results of Johansson’s Co-integration Test

Sa and Fa Sb+ and Fb+ Sb- and Fb- Sc and Fc

Test Statistic r=0 r=1 r=0 r=1 r=0 r=1 r=0 r=1

Max Eigen

Value

31.95

(.000)*

1.08

(.299)

13.56

(.064)**

.786

(.375)

15.48

(.032)*

.18

(.664)

18.81

(.009)*

.10

(.750)

Trace Statistic 33.03

(.000)*

1.08

(.299)

14.34

(.074)**

.786

(.375)

15.67

(.047)*

.188

(.664)

18.91

(.015)*

.10

(.749)

Lag length# 5 5 2 2 1 1 5 5

Note:

• r : co-integration rank of the model;

• Figures in brackets indicate the p-values;

• * denotes significance at 5% level;

• ** denotes significance at 10% level;

• # : Based on minimum values of the Schwarz Information Criteria


44

STEP 3: Testing the short run dynamics to determine which market leads and which

market follows

Vector Error Correction Model (VECM)

The VECMs for change in the forward prices and in the spot prices can be represented as under:

∆Ft = δf + αf êt-1 + βf ∆Ft-1 + γf ∆St-1 + ϵft

and

∆St = δs + αs ê t-1 + βs ∆S t-1 +γs ∆Ft-1 + ϵst

where

• êt-1 : measures how the current price of the dependent variable adjusts to the previous period’s deviation from the long

run;

• ∆St-1 and ∆Ft-1 : measures how the current price adjusts to the change in the variables in the previous period;

• The first part of model : equilibrium error (EC);

• Coefficients (αf and αs ) : Indicates the speed of adjustment in the forward prices and the spot prices respectively

The smaller the absolute value of the EC term, faster is the adjustment made by the concerned market towards equilibrium

and leads the price discovery process;

• The second part of model : represents the short run effect of the change in the prices in the previous period on current

price’s deviation.


45

Results of Vector Error Correction Model

Sa Fa Sb+ Fb+ Sb- Fb- Sc Fc

Error Correction

Coefficient

(Standard Error)

[t-statistic]

-0.540

(.343)

[-1.575]

0.773

(.144)

[5.377]*

0.103

(0.138)

[0.750]

0.246

(0.073)

[3.392]*

-0.383

(0.210)

[-1.825]

0.686

(0.202)

[3.399]*

0.235

(.305)

[0.771]

0.666

(.212)

[3.141]*

Lead/Lag Leading Lagging Leading Lagging Leading Lagging Leading Lagging

Lag length # 5 5 2 2 1 1 5 5

Note:

• Standard Error ( );

• T Statistic [ ];


• #: Based on minimum values of the Schwarz Information Criteria


46

STEP 4 : Testing the direction of causality and confirming the VECM Results

Granger Causality Test

Null Hypothesis F statistic P value

Sa does not granger cause Fa 2.130 0.163

Fa does not granger cause Sa 0.427 0.818

Sb+ does not granger cause Fb+ 7.573 0.004*

Fb+ does not granger cause Sb+ 1.766 0.201

Sb- does not granger cause Fb- 23.714 0.000*

Fb- does not granger cause Sb- 0.376 0.547

Sc does not granger cause Fc 3.334 0.064**

Fc does not granger cause Sc 1.1684 0.401

Note:


• ** denotes significance at 10% level;

• # : Based on minimum values of the Schwarz Information Criteria

Results of Granger’s Causality Test


residential forward vs spot market empirical analysis of property

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