Indonesia Residential Property Price Index

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Indonesia’s Residential Property Price Index

Prepared by Dudi Dermawan and Tri Setyoningsih

Abstract

Indonesia’s economy grew 6.49% in 2011, after 6.83% growth in 2010, despite the

global crisis. With high domestic consumption and investment and healthy export

revenues, the Indonesian government is confident that the economy will grow by 6.3%-

6.7% in 2012. GDP growth averaged 4.6% from 2000 to 2003, then 5.4% from 2004 to

2006, and finally 6.2% in 2007-2008. But economic growth never translated to strong

house price increases.

As predicted in 2012, the property market will continue to enjoy a strong growth this year

due to healthy economic growth and political stability in Indonesia. Property in

Indonesia tends to increase more than 5% which supported high demand foreign

company and new industry growth in Indonesia. Moreover, for housing sales increased

rapidly more than 15% a year with total transaction as much as Rp 58,3 trillion ($ 9.2

billion) as impact increasing purchasing power and supported with a lower interest rate

for property sector (under 9% in 2012 compare to 1997 above 20% a year).

From the Central Bank role especially to monitor financial stability and to conduct

monetary policy, property index is useful to monitor Sharp falls in residential property

prices which can have a detrimental impact on the health and soundness of the financial

sector, and on the financial situation of individuals and of individual households, by

affecting credit ratings, the value of collateral and the debt to equity ratio. The other

hand, under inflation targeting framework, the index plays in the inflationary process and

for economic performance, also its role as one of information variables in predicting

output and inflation.

Currently, to price measure property in Indonesia is using the chain method,Laspeyres

method, with the advantages requires quantity data from only the base period. This

allows a more meaningful comparison over time. But it misses both short-term

fluctuations in the composition of housing as well as long-term changes in the quality of

the typical Indonesia’s house. In the next step will propose the hedonic methodology in

theory does an excellent job of controlling for variations in house quality. But the

tremendous data required to estimate a hedonic price index limit doing so to new homes.

Economist in Real Sector Statistics Division, Department of Economic and Monetary Statistics, Bank Indonesia.

E-mail address: [email protected] and [email protected]. The views expressed in this paper are those of the author and do not necessarily reflect those of Bank Indonesia.

mailto:[email protected]

mailto:[email protected]

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1. Introduction

Prices are very useful indicators to detect booms and busts, on the real estate market like any

other market. However price evolutions can be less informative when the market shrinks and fewer

transactions take place. Very small shifts in the supply or in the demand can result in large price

variations, which are the sign of real tensions. In cases like these, information on the quantities

exchanged on the market can shed additional light on the meaningfulness of the price evolution.

Tracking property price movements in an up-to-date manner comparable to the way stock and

bond price indexes1 track the movements of those other major asset classes has long been a goal

of both academic and industry researchers focusing on real estate investments. The fundamental

problem is that property assets trade in private search markets rather than public securities

exchanges. As a result, transaction price data in real estate pertains to individual whole assets

each of which is unique and traded in a private deal between one buyer and one seller. The

individual assets (properties) are trade infrequently and irregularly through time.

To understand the behavior of housing prices and their influence on the economy, it is crucial to

have an accurate measure of aggregate housing prices. In practice, however, it is difficult to

develop such a measure. Housing is an extremely heterogeneous good, and houses are sold only

infrequently. Heterogeneity makes it difficult to distinguish between aggregate and individual price

variations. The infrequency of sales implies that, in any time period, prices are not observed for

most houses.

No single indicator of house prices can satisfy multiple uses. For instance, the price dynamics of

the housing market for monitoring house inflation, as experienced by purchasers for example, may

best be estimated by collecting information on current transaction prices. In contrast, in valuing the

housing stock when estimating aggregate wealth the sample of transacted dwellings must ideally

be supplemented by price information on non-transacted dwellings in order to get an accurate

representation.

1An index number measures the relative change in price, quantity, value, or some other item of interest from one time period to another.

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Different uses of residential property price indices2

Important uses Functions

As a macro-economic indicator of inflation

Rising house prices are often associated with periods of economic expansion while falling house prices often correspond with a slowing economy.

In fact, Goodhart and Hofmann (2006) show that for 16 industrialised countries there exists a strong correlation between house prices and economic activity.

For monetary policy and inflation targeting

Central banks in some countries turn to a Monetary Conditions Index (MCI) as a day-to-day operating target for the conduct of monetary policy;

Jarocinski and Smets (2008), and Goodhart and Hofmann (2007), the MCI would include some measure of house prices because of, they argue, the important role this variable plays in the inflationary process and for economic performance.

As a measurement of wealth

From a homeowner’s perspective, house price changes will have a significant impact on his or her wealth because, as is well known, a house is often the single largest asset or investment in that person’s portfolio.

As a financial stability or soundness indicator to measure risk exposure

Sharp falls in residential property prices, can have a detrimental impact on the health and soundness of the financial sector, and on the financial situation of individuals and of individual households, by affecting credit ratings, the value of collateral and the debt to equity ratio.

As a measurement of asset price

Asset prices relates to its role as one of information variables in predicting output and inflation. An important elements in the chain along which monetary policy stimuli are transmitted to the real economy as predictors of the future course of the economy, independently of their active role in the transmission process

2. Measurement Problems

This section explains how the interaction of the two problems interferes with measurement. The

two main problems are:

a. The tremendous heterogenity among houses

No two houses are the same. At the very least, they differ in location. They may differ in

neighborhood, city, or metro area. Even the difference of a few hundred feet can have an

appreciable price effect. Obviously, so too will other attributes, ranging from the number of

bedrooms and bathrooms. Naturally, observed differences in characteristics between two

houses will be reflected in differences in price.

2Harmonized Indices Of Consumer Prices (HICPs), Handbook On Residential Property Price Indices

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The specific combination of attributes, both locational and physical, associated with any

particular house can be thought of as corresponding to that house’s “quality.” As is intuitive,

quality captures the grade of workmanship and materials within a house. But it also meant to

capture virtually every other variation in attributes. So, for example, a two-bedroom house is of

higher quality than a one-bedroom house (all else equal). Similarly, a house in a desirable

location is of higher quality than a house in an undesirable location (all else equal).

Unfortunately, the quality associated with any specific house is not directly observable.

Otherwise, aggregate price could be measured as the price of a house of predetermined

quality.

b. The infrequency of sales

Because transactions on any specific house occur relatively infrequently, it is hard to know the

amount at which a specific house will transact today. Sales amounts for similar houses offer

some guidance. But, at the very least, differing locations affect a house’s quality.

Combined, the infrequency of sales and heterogeneity make it difficult to find a representative

sample of home prices with which to estimate aggregate prices. The main way to get sample

prices for a given time period is to look at homes that are sold in that period. But the quality of

the homes that are sold may systematically differ from the quality of the overall housing stock.

For example, it may be that one period’s sales are disproportionately skewed toward low-

quality houses thereby biasing down any estimate of aggregate prices. A different period’s

sales may be skewed toward high-quality houses. Alternatively, it may be that owners of

houses that have declined in price hesitate to sell, whereas owners of houses that have

appreciated are anxious to sell. In this case, estimates of aggregate price appreciation will be

biased upward.

3. Methodology

a. Laspeyres Method

The chain method uses the data of the last two periods to calculate a period to period chain

link index which is used to update the index level from the previous period. This method using

Laspeyres method with the advantages requires quantity data from only the base period. This

allows a more meaningful comparison over time. The changes in the index can be attributed to

changes in the price. However, Laspeyres method does not reflect changes in buying patterns

over time. Also, it may overweight goods whose prices increase.

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Where: P is the price index pt is the current price p0 is the price in the base period q0 is the quantity used in the base period

b. Hedonic Technique

One approach is referred to as “hedonic” regression. In this approach property prices are

modeled as reflecting a bundle of individual property and transaction attributes (or “hedonic

characteristics”), such as location, age, size, building quality, tenant/lease quality, type of buyer

and seller, etc. In principle, if the regression model can adequately capture all of the factors that

affect property value, then it can control

for differences in the transacting

properties’ quality across time, for

example by basing the index on a defined

representative property and representative

transaction.

Instead of assuming that a house’s quality

remains constant over time, the hedonic

statistical methodology explicitly estimates

prices for the attributes that determine

house quality. It can then “construct” and

price a hypothetical constant-quality

house, that is, one with the same

attributes over time. By choosing the

appropriate mix of attributes, this constant-

quality house is taken to be representative

of the aggregate housing stock.

One interpretation of the services that flow from any particular house is that they represent the sum

total of the services that flow from each of its many attributes. In other words, a house’s service

may represent the sum of its bedroom services, bathroom services, kitchen services, lot services,

location services, etc. If so, a house’s price would approximately equal the sum total of the price

times the quantity of each of its attributes. This interpretation implies a straightforward statistical

regression that estimates attribute prices based on the correlations between observed house

Modelling

House

Price

Index

(HPI)

Time Series

(Historical data start from 2007 in 14 provinces)

Hedonic Regression

(Multiple Regression Model)

Supporting Info “hedonic characteristics”

Location of project, amenities, area (m2), building area (m2), type of home .

Model Test

1. Optimistic

2. Most Likely

3. Pessimistic

Measuring house price with chain index

Hedonic price in view of international harmonisation

Existing condition

limited data dissemination Developing nasional

database

Lack of the availability of indicator in property to asses financial risk and vulnerability

The availability of early warning system in property to assess financial risk

Idelal Condition

Early warning system indicators to assess finacial stability

Central Bank policies

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prices and house attributes.

To estimate an aggregate price of housing, the final step is to apply the estimated attribute prices

to a set of attributes representative of the aggregate housing stock. Typically, the representative

bundle comprises the estimated average quantity of each attribute in the housing stock in some

base year. The price of the representative bundle becomes the estimate of aggregate prices. Note

that because attribute prices may change over time at different rates, the composition of the

representative bundle meaningfully affects the rate of aggregate price appreciation. Hence, so too

does the choice of base year.

The main drawback to this hedonic technique is that it requires a tremendous amount of data on

house attributes. As already discussed, the number of attributes that affect a house’s quality is

extremely large. The subjective nature of some of these attributes makes them difficult to

determine even for a single house. Obtaining the information on the attributes for the thousands of

observations necessary to estimate corresponding prices is thus a substantial challenge.

4. Indonesian Case

a. Recent Development

Indonesia’s economy grew 6.49% in 2011, after 6.83% growth in 2010, despite the global

crisis. With high domestic consumption and investment and healthy export revenues, the

Indonesian government is confident that the economy will grow by 6.7% in 2012.GDP growth

averaged 4.6% from 2000 to 2003, then 5.4% from 2004 to 2006, and finally 6.2% in 2007-

2008. But economic growth never translated to strong house price increases. In 2008 the

Indonesian house price index was still about 50% below its 1994 peak in real terms.

One possible reason for property’s weak performance is Indonesia’s unpredictable inflation

rate, typically outpacing economic growth. From the Asian crisis peak of 58% in 1998 and 20%

in 1999, inflation was wrestled down to 3.8% in 2000. In 2001 and 2002, inflation soared to

more than 11%. Then it eased to 6% in 2004. The consumer price index jumped again to

10.5% in 2005, and 13% in 2006. Inflation dipped back to 6.2% in 2007 before rising to 9.8% in

2008. Then, it eased again to 4.8% in 2009 and 5.1% in 2010. In January 2011, it again

increased to 6.9%, but steadily declined through the year and it was reported at 0.07% in

March 2012.

High inflation increase the level of uncertainty in the economy. This tends to discourage people

from borrowing to finance house purchases.While real estate investment may be used by

wealthy people as a hedge against inflation, this is not an option for the majority of the

population.

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International experience suggests that movements in real estate prices have important

implications for macroeconomic and financial stability. In Indonesia the relationship between

the property market and the wider economy is of particular significance for a number of

reasons. First, the property market plays an important role in Indonesia’s economy. Housing is

the most important form of savings for many households. In the banking sector, about 33% of

domestic credit currently comprises mortgage loans for the purchase of private residential

properties and loans for building and construction and property development. Changes in

property prices and rents influence consumer price inflation as many as 0.01%.

Secondly, property prices tend to be increased in all area in Indonesia than elsewhere, for

example Jakarta is the largest rental growth in 2010 (Cushman & Wakefield Research - Global

Real Estate Solution).The highest rental growth in Indonesia is supported by a solid economic

recovery and a stable political environment generated occupier demand and, as a result, rents

rose by almost 22% over the year.

Source: Cushman & Wakefield, February 2011 Source: Cushman & Wakefield, February 2011

Based on Credit Suisse survey 2012, Indonesia has the highest portion of respondent that wants to

purchase a house in the next two years as compare to the other emerging countries in the survey.

25% of the respondents plan to purchase a house in the next two years, as compared to only 16%

of respondents in China and 14% of respondents in Brazil. This is also supported by the positive

income and personal finance outlook among Indonesia respondents.

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Source: Credit Suisse survey 2012

Foreign ownership

Foreign ownership is difficult in Indonesia. Land titles can only be held by Indonesian citizens.

Foreign land ownership is against the constitution. For apartments, the 1996 regulation (No.

41/1996) states that foreigners who reside in Indonesia, or visit the country regularly for business

purposes, can purchase a home, apartment or condominium as long as it isn't a part of a

government-subsidized housing development.

However, foreigners can only hold land-use (hak pakai)

deeds, and most developments hold right-to-build

deeds (hak guna bangunan). It is not possible for

someone to have a land-use deed for a sub-unit of a

right-to-build deed. The length of these titles varies as

well. There in lie some of the difficulties and unclear

ownership issues.

So foreigners can effectively only lease, and not truly

own an apartment for up to 70 years, but not free

http://www.expat.or.id/info/PeraturanPemerintah41-1996-PemilikanRumaholehOrangAsing.doc



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standing houses. Within this 70-year period, foreigners must also periodically renew their right to

use. The initial land-use period is for 25 years, then renewed for an additional 25 years and finally

20 years.

Additionally, the threshold or minimum property sales price that a foreigner can purchase is 1.5

billion Indonesian Rupiah, which is around USD168,388. This minimum "purchase" price is quite

high in the Indonesian context. Foreigners may purchase a house on freehold land by written

consent from the landowner, for 25 years and extendable to a further 25 years. A mooted change

in the law on foreign property ownership would extend the leasehold period to a full 70 years as

opposed to 25 years followed by subsequent renewals, was expected at the end of 2010, but is yet

to be passed in the House of Representatives, and has encountered opposition, particularly in Bali.

Indonesia property outlook remains bullish

The Indonesia property market is set for a consecutive bullish year in 2012. Two important factors

that will play a major role in real estate double digits growth this year are Indonesia's promotion to

investment grade status and real estate foreign ownership reform. These two drivers will ensure a

strong and stable growth in Indonesia's property market for years to come. Achieving investment

grade status in 2011 (Moody’s set Baa3) means that Indonesia is recognized as a reliable and

stable borrower of funds.

Higher Foreign Direct Investment (FDI) and optimism from local corporates will boost economic

activity and growth, which in turn will positively affect the local property market. Moreover for

foreign ownership, relaxation of foreign ownership restriction on real estate in Indonesia has been

intensely advocated by both local and foreign interest parties, such as REI and FIABCI.

Comparing to other major cities in Asia, Jakarta property market is still undervalued, but with higher

rental yields. A more relaxed foreign ownership lay will certainly boost the overall property market

in Indonesia. As predicted in 2012, the property market will continue to enjoy a strong growth this

year due to healthy economic growth and political stability in Indonesia. Property in Indonesia tend

to increase more than 5% which supported high demand foreign company and new industry growth

in Indonesia. Moreover, for housing sales increased rapidly more than 15% a year with total

transaction as much as Rp 58,3 trillion ($ 9.2 billion) as impact increasing purchasing power and

supported by a lower interest rate for property sector (under 9% in 2012 compare to 2007 above

20% a year).

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Indonesia’s Property Forecasting 2012 – 2014

Source: Residensial Property Survey, Bank Indonesia

b. Residensial Property Price Index (RPPI)

Residential Property Prices in quarter IV-2011 accelerated for

all types of buildings. Residential Property Price Survey in 14 cities

noted that residential property price index in quarter IV-2011 stayed at

143,55, moved 1,15% (qtq), higher than previous increase (0,48%, qtq).

Most of respondents (37,81%) revealed that the acceleration in the

residential property prices were attributableby the rising of construction

materials price3, higher of worker’s salaries (19,10%) as well as the higher

of permit fee (18,31%).

3

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In Quarterly(qtq) basis, the highest price increase was at

small type of building. Compared with previous period, the survey

recorded that all types of buildings experienced price hike with the highest

at small type of building (1,72%, qtq). By the city surveyed, Medan still

led the increase by 3,19%, especially at large type of building (3,66%).

Increased rates also occurred in Jabodebek and Banten area, with the

small type of building led the increase (2,50%).

Residential property prices also moved higher from the same

period at the previous year. Residential property price index mounted

5,05%, higher than the price increase over the same period in previous

year (4,54%). Describe by the type of building, price escalated in all types

of buildings, with the small type of building led the growth by 5,98%.

Observed by region, the highest price increase occurred in Jabodebek

and Banten (6,70%), mainly on small type of building (8,06%). In Makasar,

residential property prices increased by 6,04%, lower than the same

period in previous year (8,11%), with the medium type of building led the

growth by 9,95%.

Mild pressure on residential property price were predicted to

continue in quarter I-2012. In quarterly basis (qtq), pressure on

residential property price is predicted to a slower growth by 0,83%, with

the highest price increase in small type of the house (1,22%), while in

regional basis price hike most in Bandar Lampung (2,01%).


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In Annual basis (yoy), residential property prices in quarter I-

2012 were estimated to increase in a lower growth compared to previous

quarter in the amount of 3,60%. In regional basis, accelerating price was

predicted in Medan (5,38%) and Makasar (5,20%).

In Comparison with the housing group index in the

Consumer Price Index (CPI) for quarter IV-2011, residential property

price appeared to move in the same direction. Housing group price

index along quarter IV-2011 (Des 2011) grew 0,98% higher than the

previous quarter (0,96%). Therewith, Residential Property Price Index

(RPPI) also moved by 1,15% (qtq), higher than previous quarter (0,48%,

qtq).


c. Hedonic Model Price

Hedonic model of price is calculated based on result of quarterly residential survey in 14

reference regions. Each Region has following characteristics4:

a. Type of building: Small, medium and large

b. Locational characteristics: city, suburb, and urban fringe

4See appendix A2 for more details.

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c. Impor content of building material: mechanical and electrical equipment, glass, sanitair,

and granite.

The model is following:

Table 1

Log of land price, import content of building material, log of building size, location, and log of land size in 14 Regions

Variable Coefficient Std. Error t-Statistic Prob. LOG(LP) 0.270987 0.003951 68.59270 0.0000

IMPORT 0.056364 0.008171 6.898010 0.0000 LOG(BS) 0.841893 0.009398 89.58573 0.0000

LOCATION 0.247099 0.016160 15.29045 0.0000 LOG(LS) 0.447141 0.013294 33.63443 0.0000

C 9.795924 0.066533 147.2335 0.0000 R-squared 0.786928 Mean dependent var 19.38057

Adjusted R-squared 0.786842 S.D. dependent var 0.867571 S.E. of regression 0.400550 Akaike info criterion 1.008527 Sum squared resid 1982.396 Schwarz criterion 1.012129

Log likelihood -6227.705 Hannan-Quinn criter. 1.009734 F-statistic 9126.746 Durbin-Watson stat 0.721506

Prob(F-statistic) 0.000000

Log(HP) = 9.795924 + 0.270987 log(LP) + 0.056364 IMPORT + 0.841893 log(BS) + 0.247099

LOCATION + 0.447141 log(LS).

Two broad results are noteworthy. First, the model fit the data reasonably with the fully specified

versions explaining 78,69 per cent of the variation in log prices of land (Rupiah), building size (M2),

and land size (M2), locational characteristics, and import content of building material. Second,

nearly all of the coefficients have the expected signs and are statistically significant. The most

important characteristics in explaining log prices in this regression is building size and land size.

Two examples models for Jakarta, Medan, and Surabaya are presented in Table 4, 5, 6.

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5. Summary and Recommendation

5.1 Summary

a. The Indonesia property market is set for a consecutive bullish year in 2012, and It has the highest portion of respondent that wants to purchase a house in the next two years as compare to the other emerging countries. Beside that, property in Indonesia tend to increase more than 5% which supported high demand foreign company and new industry growth in Indonesia. Moreover, for housing sales increased rapidly more than 15% a year with total transaction as much as Rp58,3 trillion ($ 9.2 billion).

b. Data of Residential Property Prices Index (RPPI) is a result of quarterly residential property price survey which is conducted on 1999. The survey based on development project from 13 cities, i.e. Medan Padang, Palembang, Bandar Lampung, Bandung, Semarang, Yogyakarta, Surabaya, Denpasar, Banjarmasin, Manado, Makassar, and Jabotabek (Jakarta, Bogor, Tangerang, and Bekasi) area, which consist of around 215 developers in 13 Bank Indonesia Regional Offices. The same data of RPPI is used for running hedonic model.

c. The methodological framework result for House Price Indices in Indonesia, as follows:

Residential Property Prices Index (Laspeyres method) The housing group index in the Consumer Price Index (CPI) in end-of 2011, residential property price appeared to move in the same direction and its Prices accelerated for all types of buildings. Residential Property Price Index is remain stable at 143,55 with the possibility to incresed in the near term. The accelerated of the residential property prices were attributable by the rising of construction materials price, higher of worker’s salaries as well as the higher of permit fee.

Hedonic Model Price of House Hedonic Model Price of House generally shows that building size (M2) and land size (M2) significantly affect price of house in 14 reference regions in Indonesia. Moreover, location and import content for raw materials of house will impact price of house. Except for Medan and Surabaya5are negatively impacts of import content to the house price.

5.2 Recommendation

In the light of the above explanations we recommend that hedonic price methodology be implemented to refine chain index methodology in measuring house price. Hedonic approach provide useful estimates of pure price changes and information about housing characteristics in Indonesia ie size of dwelling (land and building), locational characteristics, and building materials. Notwithstanding, in forming assessments of the housing market and the broader economy, it is still desirable to be able to provide the extensive additional valuable information from the available data.

5Medan and Surabaya are the highest demand of housing in Indonesia. To increase the market share, the developers in both areas are offering highest quality of house (large import content) as part of their marketing strategy.

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References

1. Harmonized Indices Of Consumer Prices (HICPs), Handbook On Residential Property Price

Indices.

2. Bank Indonesia’s property survey and report 2011.

3. Chain Indices of the Cost of Living and the Path-Dependence Problem: An Empirical Solution,

Nicholas Oulton, London School of Economics.

4. A Guide to Aggregate House Price Measures, Jordan Rappaport

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Appendix

Table A.2 The Residensial Property Data Captured from Existing Survey

Data Category Explanation

Analytical framework, concepts, definitions, and classifications

Data of Residential Property Prices Index (RPPI) is a result of quarterly residential property price survey which is conducted on 1999 to get information on the development of residential property prices, both are for the current quarter and prediction for the next quarter. The survey based on development project from 12 cities, i.e. Medan Padang, Palembang, Bandar Lampung, Bandung, Semarang, Yogyakarta, Surabaya, Denpasar, Banjarmasin, Manado, and Makassar. At the first quarter 2002, coverage area of survey becomes 13 cities including Jabotabek (Jakarta, Bogor, Tangerang, and Bekasi) area. In the first quarter 2004, coverage became 14 cities including Pontianak. Since 2007 the coverage survey area has been extended including Banten area (Serang and Cilegon). Data collected directly from developer covered price (house and land) and total sales in the recent quarter and prediction for the next quarter, which is calculated using a simple chain index method.

Scope of the data

The involving respondent consist of 47 major developers in Jabodebek-Banten and around 215 developers in 13 Bank Indonesia Regional Offices, who are considered as the market leaders in their areas. For Jabodebek-Banten, the chosen respondents are developers whose areas of development covered more than 20 hectares. The result of the survey comprises the data on types of house, prices (house and land) in the current quarter, provisional figures for the next quarter, and the number of sold houses by the property companies/developers as the producer of housing. Types of house are classified based on the width of the building, namely: small type of building (≤ 36 m2), medium type of building (> 36 m2 to 70 m2), and large type of building ( ≥ 70 m2).

Accounting Conventions

Data collections are conducted on quarterly basis by the following means: (1) direct interview to the respondents (developers) and/or (2) sending questionnaire to the respondents. The data collection for Jabodebek-Banten area is conducted by a consultant/surveyor appointed by Bank Indonesia, while the survey in Bank Indonesia Regional Offices areas are conducted by the concerning Bank Indonesia Regional Offices themselves as well as the third appointed parties. RPPI is a composite prices data at 47 developers Jabodebek-Banten and 215 developers at 13 Bank Indonesia Regional Offices

Compilation Practices

The result of RPPI are processed and presented as index (RPPI) by using chain index method of the following formula.

RPPIt = RPPIt-1 + (RPPIt-1 x Pt/100) Whereas RPPIt : RPPI during t period RPPIt-1: RPPI during t-1 period

Pt : price average t– price average t-1

Other Aspects

RPPI has already experienced 2 basic year changes, namely: First Quarter 1999 to Fourth Quarter 2002 by using basic year of First Quarter 1999 I-1999 = 100) and the presented data was a composite of 12 cities data (not including Jabotabek area). Since First Quarter 2003, Jabotabek area was included in the Composite Index of 13 cities and by using the basic year of First Quarter 2002 = 100. Starting from the first quarter 2004, Pontianak has been added; meanwhile since 2007 Jabotebek area has been enlarged to Banten (Serang and Cilegon) area, therefore, the Composite Index now covered 14 cities, RPPI still applying the basic year of First Quarter 2002 = 100

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Table A.3

The Characteristics of Residensial Property Data in 14 Reference Regions

Table A4

Log of land price, import content of building material, log of building size, location, and log of land size in Jakarta

Variable Coefficient Std. Error t-Statistic Prob. LOG(LP) 0.503746 0.012514 40.25609 0.0000

IMPORT 0.200297 0.350694 0.571145 0.5680 LOG(BS) 0.566062 0.014264 39.68545 0.0000

LOCATION 0.044963 0.202291 0.222267 0.8241 LOG(LS) 0.597180 0.018775 31.80792 0.0000

C 7.154021 0.331004 21.61309 0.0000

R-squared 0.893712 Mean dependent var 19.80094 Adjusted R-squared 0.893491 S.D. dependent var 0.875204 S.E. of regression 0.285629 Akaike info criterion 0.334245 Sum squared resid 195.9650 Schwarz criterion 0.348663 Log likelihood -396.4307 Hannan-Quinn criter. 0.339489 F-statistic 4039.401 Durbin-Watson stat 0.929267 Prob(F-statistic) 0.000000

Jakarta 409 934 990 Y/N Y/N Y/N Y/N Y/N Y/N

Surabaya 82 259 189 Y/N Y/N Y/N Y/N Y/N Y/N

Medan 74 136 81 Y/N Y/N Y/N Y/N Y/N Y/N

Bandung 141 169 87 Y/N Y/N Y/N Y/N Y/N Y/N

Yogyakarta 230 517 451 Y/N Y/N Y/N Y/N Y/N Y/N

Semarang 405 624 294 Y/N Y/N Y/N Y/N Y/N Y/N

Padang 58 95 13 Y/N Y/N Y/N Y/N Y/N Y/N

Palembang 41 76 21 Y/N Y/N Y/N Y/N Y/N Y/N

Makasar 66 131 79 Y/N Y/N Y/N Y/N Y/N Y/N

Bandar lampung 48 114 74 Y/N Y/N Y/N Y/N Y/N Y/N

Denpasar 80 125 76 Y/N Y/N Y/N Y/N Y/N Y/N

Manado 26 104 62 Y/N Y/N Y/N Y/N Y/N Y/N

Banjarmasin 133 348 91 Y/N Y/N Y/N Y/N Y/N Y/N

Pontianak 36 27 21 Y/N Y/N Y/N Y/N Y/N Y/N

Y/N

Y/N

RegionsSmall Medium Large City Suburb Urban Fringe

Y/N

Y/N

Y/N

Y/N

Y/N

Y/N

Y/N

Y/N

Y/N

Y/N

Y/N

Property Size (M2) Locational Characteristics

Mechanical and Electrical

Equipment

Impor Content

Y/N

Glass Sanitair Granite

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Table A5 Log of land price, import content of building material, log of building size, location, and log of land size

in Medan Variable Coefficient Std. Error t-Statistic Prob. LOG(LP) 0.237707 0.020020 11.87337 0.0000

IMPORT -0.123540 0.039510 -3.126821 0.0018 LOG(BA) 0.760437 0.025767 29.51161 0.0000

LOCATION 0.067299 0.025530 2.636112 0.0085 LOG(LA) 0.286071 0.029381 9.736732 0.0000


Adjusted R-squared 0.829408 S.D. dependent var 0.842593 S.E. of regression 0.348014 Akaike info criterion 0.732477 Sum squared resid 128.1386 Schwarz criterion 0.760503

Log likelihood -383.6780 Hannan-Quinn criter. 0.743097 F-statistic 1034.647 Durbin-Watson stat 0.883091

Prob(F-statistic) 0.000000

Table A6 Log of land price, import content of building material, log of building size, location, and log of

land size in Surabaya Variable Coefficient Std. Error t-Statistic Prob. LOG(LP) 0.484612 0.010219 47.42327 0.0000

IMPORT -0.083925 0.291286 -0.288118 0.7733 LOG(BS) 0.792882 0.016997 46.64824 0.0000

LOCATION 0.198627 0.039085 5.081977 0.0000 LOG(LS) 0.413374 0.024894 16.60529 0.0000


Adjusted R-squared 0.899645 S.D. dependent var 0.911441 S.E. of regression 0.288734 Akaike info criterion 0.355415 Sum squared resid 244.9333 Schwarz criterion 0.367618 Log likelihood -517.1705 Hannan-Quinn criter. 0.359808 F-statistic 5277.576 Durbin-Watson stat 0.801638 Prob(F-statistic) 0.000000

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Indonesia Residential Property Price Index