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The Housing Market across the Greek Islands

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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The Housing Market across the Greek Islands

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  1. The Housing Market across the Greek Islands Dimitra Kavarnou University of Reading d.kavarnou@pgr.reading.ac.uk

  2. A bit more of Geography… Dimitra Kavarnou - Henley Business School, University of Reading

  3. 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

  4. North East Aegean Sea Islands Ionian Islands Sporades Islands Argo Saronic Islands Cyclades Islands Dodecanese Islands Dimitra Kavarnou - Henley Business School, University of Reading

  5. Size… Dimitra Kavarnou - Henley Business School, University of Reading

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. Data - IV Spotting the properties in Google Earth (approximately) Dimitra Kavarnou - Henley Business School, University of Reading

  17. Data - V Dimitra Kavarnou - Henley Business School, University of Reading

  18. Data - VI Calculating time distances in Google maps to port: to airport: Dimitra Kavarnou - Henley Business School, University of Reading

  19. Data Analysis - II Dimitra Kavarnou - Henley Business School, University of Reading

  20. 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

  21. 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

  22. Descriptive Statistics – of the Groups Dimitra Kavarnou - Henley Business School, University of Reading

  23. Results OLS - Groups DimitraKavarnou - Henley Business School, University of Reading

  24. Results Fixed Effects- Groups Dimitra Kavarnou - Henley Business School, University of Reading

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  30. Thank you Dimitra Kavarnou - Henley Business School, University of Reading

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