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The Housing Market across the Greek Islands. Dimitra Kavarnou University of Reading d.kavarnou @ pgr . reading.ac.uk. A bit more of Geography…. A bit more of Geography…. > than 6,000 islands/isles 117 inhabited islands 79 islands >100 (population) 53 islands>1,000.

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a bit more of geography
A bit more of Geography…

Dimitra Kavarnou - Henley Business School, University of Reading

a bit more of geography1
A bit more of Geography…
  • >than 6,000 islands/isles
  • 117 inhabited islands
  • 79 islands >100 (population)
  • 53 islands>1,000

Dimitra Kavarnou - Henley Business School, University of Reading

slide4

North East Aegean Sea Islands

Ionian Islands

Sporades Islands

Argo Saronic Islands

Cyclades Islands

Dodecanese Islands

Dimitra Kavarnou - Henley Business School, University of Reading

slide5
Size…

Dimitra Kavarnou - Henley Business School, University of Reading

attributes of the housing market i
Attributes of the Housing Market - I
  • Heterogeneity
  • Heterogeneity in many levels

Property/ Neighbourhood/ Settlement (Villages/towns)/ Islands/ Groups of Islands

  • Housing Submarkets

Islands – Groups of Islands

2. Durability

  • Community formation (trade/ piracies/ occupations/ wars)
  • Horizontal ownership (bequests)
  • Ownership rate 80% - Cultural Aspect
  • Financial CourseBooms and Recessions

Dimitra Kavarnou - Henley Business School, University of Reading

attributes of the housing market ii
Attributes of the Housing Market - II

3. Political Economy

  • Plethora of laws/ rules/ regulation
  • Tax Regime continuously changing – Unstable
  • New Laws and additional taxation established and applied from 01-01/2014
  • Taxation constitutes a Disincentive to be a homeowner – investor

4. Transactional Costs

  • High Transaction Costs
  • Among the highest in EU

Dimitra Kavarnou - Henley Business School, University of Reading

attributes of the housing market iii
Attributes of the Housing Market - III
  • Imperfect Information
  • Lack of an well-organised information institution
  • Insufficient National Cadastre
  • The area of the islands is not mapped
  • Competitive, unprofessional, non-accredited brokerage industry
  • Immovability
  • Any real estate/housing market is immovable
  • Increased demand for spatial development (amenities, infrastructure, employment, etc.)
  • More evident to islands which have physical boundaries

Dimitra Kavarnou - Henley Business School, University of Reading

attributes of the housing market iv
Attributes of the Housing Market – IV

7. External Effects

Main focus on the heterogeneity in island/ group of islands level:

  • the public amenities (Presence of public amenities on the islands/ distance from properties)
  • Port (distance)
  • Hospital (presence)
  • Airport (presence and distance)
  • University (presence)
  • Tourism rate
  • Luxury rate
  • Density (population/ geographical size)

Dimitra Kavarnou - Henley Business School, University of Reading

hedonic research idea
Hedonic Research: Idea
  • This research assesses and analyses the variables that compose the house prices in the islands of Greece
  • This research examines the impact/ significance of local public amenities on house prices across 36 islands of Greece
  • The model controls for several structural and locational characteristics of the properties as well as economic and demographic attributes of the islands

Dimitra Kavarnou - Henley Business School, University of Reading

methodology i
Methodology - I
  • Hedonic Regression Method

(The method that decomposes the dependant variable under the scope into its constituent characteristics, and obtains assessments of the contributory value of each specific characteristic)

(Rosen; 1974, Roback;1982, Bajari and Benkard; 2005)

In this research, the dependant variable (Y) is the Assessed Housing Prices - AHP or P for every property (i) , island(j), group of island(k)

Pi,j,k = α + ∑β Xi,j,k + εi,j,k

In order to mitigate the problem of heteroskedasticity as well as to compare percentage-wise the effect on the Assessed Housing Prices

(1) log(Pi,j,k)= α + ∑β Xi,j,k + εi,j,k

Dimitra Kavarnou - Henley Business School, University of Reading

methodology ii
Methodology - II

But

Τhere are also island characteristics for each island (j):

(2) log(Pi,j,k) = α + ∑βXi,j,k + ∑γZj,k + εi,j,k

Controlling the Fixed Effects for each island:

(3) log(Pi,j,k) = α + ∑βXi,j,k + δj + εi,j,k

(Boundary fixed effects model: Black;1999, Clapp, Nanda and Ross; 2008)

where δ is the total unobserved effects for each island (j) - dummies

Dimitra Kavarnou - Henley Business School, University of Reading

data i
Data - I
  • Two files from the Bank of Greece including properties in the islands that have been evaluated from 2005-2013 with property characteristics:

The property characteristics (Xi,j,k) included are:

  • Some details about the property location (not exact)
  • The living space (m2)
  • The land area (m2)
  • The date/year of permit, completion, evaluation
  • The property type (flat/detached house/ maisonette) and the floor
  • Some information about the construction quality, the neighbourhood, the view (limited)
  • Some information about the store rooms and the parking spaces

Dimitra Kavarnou - Henley Business School, University of Reading

data ii
Data - II

Limitations of the dataset

  • Not exact location (address/number, to many cases only local toponyms of settlements)
  • Either because of incomplete dataset from the estimators

But

  • Mainly because the properties in the Islands do not have an address themselves but they refer to the closest village/settlement
  • With this very limited information about their location, it was VERY difficult and time-consuming to spot the properties and calculate their distances from the amenities (ports/airports)
  • Lots of missing/ incomplete values from the evaluators (view, land, year of completion/permit)

DimitraKavarnou - Henley Business School, University of Reading

data iii
Data - III
  • Data Set Cleaning:

Out of the 14,937 properties I received, I excluded:

  • 3,620 properties in Evvoia and Crete (separate analysis – research)
  • 850 approx. duplications
  • 500 approx. did not concern properties on islands (incorrect entries)
  • 3,000 approx. to which the land area was not available
  • 300 approx. to which the year of completion or the year of permit was not available (not able to calculate the age of the property)
  • 300 approx. concerned islands with population<1,000p. or islands with insufficient number of observations/island (<15)

6,350 properties approx. in 36 islands to be spotted and calculated

  • 2,000 properties approx. not able to spot/ find the approx. location

of the closer village in Google Earth/ Google maps

4, 369 properties spotted in the final dataset

Dimitra Kavarnou - Henley Business School, University of Reading

data iv
Data - IV

Spotting the properties in Google Earth (approximately)

Dimitra Kavarnou - Henley Business School, University of Reading

data v
Data - V

Dimitra Kavarnou - Henley Business School, University of Reading

data vi
Data - VI

Calculating time distances in Google maps

to port: to airport:

Dimitra Kavarnou - Henley Business School, University of Reading

data analysis ii
Data Analysis - II

Dimitra Kavarnou - Henley Business School, University of Reading

data analysis iii
Data Analysis - III
  • Deflation of Assessed Housing Prices

The Prices are deflated and expressed in December 2012 prices:

where:

HICPDec2012= 123.28

HICPt = the HICP of the month year of the evaluation

(Source of the HICP tables: Hellenic Statistic Authority)

  • Dummy Variables Xi,j,k for the property types:
  • Flat
  • Detached House
  • Maisonette

Dimitra Kavarnou - Henley Business School, University of Reading

data analysis iv
Data Analysis - IV
  • Dummy Variables (Zj,k) for controlling:
  • The Presence of Airport on the island
  • The Presence of Prefectural General Hospital on the island
  • The Presence of University on the island
  • Dummy Variables (δj) for the fixed effects - controlling the unobserved heterogeneity of the islands (one dummy for each island)
  • Dummy Variables (Xi,j,k) for controlling:
  • View (whether the property has view to the sea or not)
  • Proximity to Capital (whether the property is located in the island’s capital or not)
  • Coastal Settlement (whether the property is located in a coastal settlement or not)

DimitraKavarnou - Henley Business School, University of Reading

descriptive statistics of the groups
Descriptive Statistics – of the Groups

Dimitra Kavarnou - Henley Business School, University of Reading

results ols groups
Results OLS - Groups

DimitraKavarnou - Henley Business School, University of Reading

results fixed effects groups
Results Fixed Effects- Groups

Dimitra Kavarnou - Henley Business School, University of Reading

results i individual islands
RESULTS –I Individual Islands
  • For 33/36 islands the living space is positively very significant to the prices

(1% significance level) while the rest 3 islands (are the islands with very small sample 15-21 obs)

1% increase in living space0.34-1.07% increase to the prices ( 0.72% increase - weighted average)

  • For 21/36 islands the land space is positively (very) significant (1% or 5%)

1% increase in the land area 0.07-0.50% increase to the prices

(0.14% increase - weighted average)

  • The Property Utilisation Ratio is relatively not significant for most of the islands (gardens/yards not significant – only in 6 islands)
  • The floor number is relatively not significant for most of the islands (only in 6 islands)

Dimitra Kavarnou - Henley Business School, University of Reading

results ii
RESULTS - II
  • The property type (flats/detached houses/ maisonettes) seems to be very significant for some of the islands

Detached housesto 14/36 islands negatively very significant (1-5%) compared to flats

i.e. The flats are moreexpensive compared to detached houses

Maisonettes to 5/22 islands negatively very significant (1-5%) compared to flats

i.e. The flats are more expensive compared to maisonettes

Maisonettes to 4/22 islands positivelyvery significant (1-5%) compared to flats

i.e. The flats are less expensive compared to maisonettes – probably because of their construction/ property characteristics/ extra facilities/ landscape

  • The Age is negatively very significant (1-5%) for most of the islands (26/36)

Every Additional Year 0.3-1.4% decrease of house prices

(0.7% decrease - weighted average)

Dimitra Kavarnou - Henley Business School, University of Reading

results iii
RESULTS – III
  • View

For 23/36 islands the view is positively (very) significant (1-5% significance level)

Approx. 13.1 – 64.2% more expensive compared to the properties without view (30.2% weighted average)

  • Proximity to Capital

For 7/36 islands the PtCapital is positively (very) significant (1-5% significance level) while for 2/36 islands the PtCapital is negatively (very) significant (1-5% significance level)

8/36 Approx. 30.3% (weighted average) more expensive

2/36 Approx. 25.2% (weighted average) less expensive

  • Coastal Settlement

For 5/36 islands if the property is located in a coastal settlement, it is positively (very) significant (1-5% significance level)

Approx. 15.3% more expensive compared to a non coastal settlement

while for 1/36 islands Coastal Settlement is negatively (very) significant (1-5% significance level)

Dimitra Kavarnou - Henley Business School, University of Reading

results v
RESULTS – V

Time Distance to Port:

  • For the bigger islands (big distances) the time distance to the port is negativelyvery significant (1-5%) - the closer to the port, the more expensive (Corfu, Kefallonia, Rhodes)- apart from specific cases (eg. Lesvos)
  • For the smaller islands (not very big in size) the time distance to the port was not very significant - apart from specific cases (eg. Paros – Milos – Salamina commuting purposes)
  • 11/36 islands showed significance in some of the regressions

Where: 10-13 had negative significance (the less the time - the closer to the port- the more expensive)

While: 3/13 had positive significance (the less the time – the closer to port – the less expensive)

Dimitra Kavarnou - Henley Business School, University of Reading

results vi
RESULTS – VI

Time Distance to Airport:

  • For some of the islands the time distance to the airport is positively very significant (1-10%) – the closer to the airport the less expensive - apart from specific cases (eg. Milos) - Probably because of the noise and disturbance.
  • 8/22 islands showed significance in some of the regressions

Where: 5/8 had positive significance (the less the time - the closer to the airport- the less expensive)

While: 3/8 had negative significance (the less the time – the closer to airport – the more expensive)

Dimitra Kavarnou - Henley Business School, University of Reading

slide30

Thank you

Dimitra Kavarnou - Henley Business School, University of Reading

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