Housing rent market: the relevance of a micromarket Paloma Taltavull de La Paz University of Alicante, Spain ERES Yearly Conference Eindhowen, June, June, 2011

Paper topic Rent market in Spain is very small: 11,2% of total family homes (census 2001) Perception of a tiny market: –Retrictions to mobility –Inefficiencies in housing market Is that true? Has the market balance in some way to develop micro markets on rent? June, 20112

Agenda Introduction Aim of the paper General overview of Spanish rent market Data Models: factorial analysis Discussion and conclusions 3June, 2011

Research questions Small rent market.. To who?. House mkt segmented? Type of house rented The role of a small rent market to: Cover mobility needs of population/work market Cover housing needs June, 20114

Findings Small in average but larger in urban areas Covering mobility needs 2 main types of houses rented 5 types of household renting homes Factors affecting mobility and quality.. Supply June, 20115

Data description Census 2001, microdata observations for all Spain, –Statistically significant of all market Information including: –House characteristics… 33, 31 usable –Construction history – Demand features of households renting houses June, 20116

Methodology 2 steps: –Cluster analysis to define the typical house rented –Factor analysis to find the ‘typical’ household renting homes in Spain June, 20117

RENT MARKET IN SPAIN June, 20118

Introduction 9June, 2011

General overview 10June, 2011

General overview June,

General overview. Spatial distribution June, Main provinces Smaler rent market’s provinces

General overview June,

General overview: Segmentation June,

Covering mobility RENT AND MOBILITY RANKING (Ratio between new residents and rented houses) Source: INE ProvincesProvincias 19-Guadalajara2,082,4005-Ávila0,870,91 03-Alicante/Alacant1,571,8325-Lleida0,660,90 45-Toledo1,441,5635-Palmas (Las)1,000,89 04-Almería1,491,5040-Segovia0,810,87 26-Rioja (La)1,371,4041-Sevilla0,930,86 16-Cuenca1,371,3509-Burgos0,720,84 46-Valencia/València1,221,3420-Guipúzcoa0,770,82 44-Teruel1,101,3427-Lugo0,770,79 12-Castellón/Castelló1,191,2921-Huelva0,880,79 31-Navarra1,241,2638-Santa Cruz de Tenerife0,810,77 30-Murcia1,251,2107-Balears (Illes)0,750,74 01-Álava1,061,1637-Salamanca0,900,70 39-Cantabria1,151,1334-Palencia0,800,69 43-Tarragona1,241,1336-Pontevedra0,690,66 42-Soria1,081,1024-León0,65 48-Vizcaya1,071,0923-Jaén0,780,65 28-Madrid1,09 15-Coruña (A)0,680,64 22-Huesca0,841,0414-Córdoba0,63 13-Ciudad Real0,921,0110-Cáceres0,640,59 02-Albacete0,931,0008-Barcelona0,540,56 32-Ourense1,070,9706-Badajoz0,590,49 49-Zamora1,000,9751-Ceuta0,530,48 17-Girona0,870,9533-Asturias0,490,46 29-Málaga0,930,9211-Cádiz0,490,44 47-Valladolid0,740,9150-Zaragoza0,430,44 18-Granada0,900,9152-Melilla0,350,32 June,

General overview: Distribution and mobility June,

Stable living house? June, YEAR OF ARRIVAL TO RENT HOUSE, SPANISH MARKET ,5 10,39 10,19 7,34 5,12 4,20 10,48 12,50 10,74 7,27 4,73 1,51 0,0 2,0 4,0 6,0 8,0 10,0 12,0 14,0 16,0 18, Antes de 1941 RENT ( % of total family houses in rentr) Source. INE, Censo

June,

SOME RENT HOUSIGN SUPPLY CHARACTERISTICS June,

June, Distributed along groups with lower size

June, RENT STOS BY AGE OF THE BUILDING. SPANISH HOUSING STOCK ,00 10,00 20,00 30,00 40,00 50,00 60,00 70,00 80,00 90,00 TOTAL 01- Á lava 02-Albacete 03-Alicante/Alacant 04-Almer í a 33-Asturias 05- Á vila 06-Badajoz 07-Balears (Illes) 08-Barcelona 09-Burgos 10-C á ceres 11-C á diz 39-Cantabria 12-Castell ó n/Castell ó 51-Ceuta 13-Ciudad Real 14-C ó rdoba 15-Coru ñ a (A) 16-Cuenca 17-Girona 18-Granada 19-Guadalajara 20-Guip ú zcoa 21-Huelva 22-Huesca 23-Ja é n 24-Le ó n 25-Lleida 27-Lugo 28-Madrid 29-M á laga 52-Melilla 30-Murcia 31-Navarra 32-Ourense 34-Palencia 35-Palmas (Las) 36-Pontevedra 26-Rioja (La) 37-Salamanca 38-Santa Cruz de 40-Segovia 41-Sevilla 42-Soria 43-Tarragona 44-Teruel 45-Toledo 46-Valencia/Val è ncia 47-Valladolid 48-Vizcaya 49-Zamora 50-Zaragoza Before After 1991 ( % total rent houses) Source. INE, Censo 2001

General overview: Quality June,

SOME DEMAND CHARACTERISTICS OF RENT MARKET June,

Model 2- data description June,

Model 2- data description June,

Model 2- data description June,

Model 2- data description June,

Model 2- data description June,

Model 2- data description. Rent by nationality RENT TENENCY AND NATIONALITY (% of total population living in rent) Spanish79,60 European without Spanish4,81 Áfricans4,87 Latinamericans9,80 Asians0,92 Others0,02 Source. INE, Censo 2001 June,

Model 2- data description RENT TENENCY AND NATIONALITY (% of total population living in rent) Spanish79,60 European without Spanish4,81 Áfricans4,87 Latinamericans9,80 Asians0,92 Others0,02 Source. INE, Censo 2001 June,

Model 2- data description. Mobility and rent market June,

Model 2- data description June,

Model Rent market suplly/demand –… 44 variables..profile of household in rent –22 about the head of household –22 about structure of the household –With 22 house characteristics –92,088 observations microdata Cluster analysis … types? Supply groups of houses by characteristics Households…Demand groups Factorial analysis… Determinants of rent June,

Supply. Rent house type Average house… characteristics –Rented since 1992 –Good neighbourhood quality –Good communications, enough public services –Low delinquency –Low green areas –Around 81 m2, 4 bedrooms, new –Good quality June,

Model 1. Supply. 2 cluster identified: 2 types of houses: June,

Model 1. Supply. Centroids June,

Model 2 Factors explaining supply side of rent market Factors explaining housing rent. The supply side Factor 1- Type of building Númber of floors0,88 Factor 7 – Water system KMO and Bartlett's Test Floor of the apartment0,79Water system0,82 Kaiser-Meyer-Olkin Measure of Sampling Adequacy.0,75 Type of building0,75Water distribution network0,78 Bartlett's Test of SphericityApprox. Chi-Square14724,87 Type of owner0,59Factor 8 – Building quality df378 Number of garaje parking0,51Building quality0,71 Sig.0 Factor 2- Problems in the neighbourhoodBad road network0,52 Contamination0,66 Matrix Determinant0,012 Not well cleaned streets0,64Factor 9- Accesibility Noises0,63Vehicles availabilty (cars..)0,7 Delinquency, insecurity0,58 Total Varianza explicada No green space0,47 Componentes- FactoresInitial Eigenvalues Bad accesibility0,3 Total% de la varianze% Acc Factor 3- Size 13,8513,75 Nº of rooms0,87 21,997,1120,86 Surface0,86 31,685,9926,85 Factor 4- Year of arrival 41,595,6932,55 Year of arrival to city0,83 51,314,6737,22 Year of arrival to house0,77 61,224,3741,59 Factor 5- Energy 71,134,0445,63 Type of energy used0,77 81,093,8949,52 Gas0,59 91,083,8553,36 Factor 6- Facilities Telephone0,64 Hitting0,61 *Coeficientes de la matriz de componentes rotados Método de extracción: componentes principales, rotación: Varimax con normalización de Kaiser June,

Demand Household renting house ‘average-type’ –Spanish, male, around 35 years old –Living in hte municipality from more than one decade –Married, 3 – 4 people: couple plus children –Working in the same municipality. 2 workers 20 minutes commuting Permanent contract, working for other –Bachelor level –Good quality in the house June,

Model 1. Demand…5 Clusters identified Three foreign households –1.- Single immigrant men (Latinoamerica) living with others, young, medium education level High mobility, commuting 1.5 hours Recent arrived, working more hours than average Good quality in house neighbourhood, 80 m2 –3.- Single female also immigrant (Latinoamerica), medium age, low education level Living with other person (child or partner) No recent arrived (moving from other city), working little hours No so good quality in neighbourhood –2. Married man, from Europe (main UK) High education level, working in services Working in the city, stable work High quality in living conditions, large houses June,

Model 1. 5 Clusters identified 2 Spanish rent households. –4. Single man, 37 years old, medium education Not migrant (living in the municipality from long time) Working in companies link to services, medium level salary Good quality in the neighbourhood, 70 m2 –5. Single man, 45 years old, high level of education Moved to municipality long time ago Commuting in own car Good or very good quality in neighbourhood June,

Model 2: Household factors determinants of rent a house. Demand side June,

Model 2: factor analysis June,

Conclusions Rent market in Spain –Census 2001 data: stock&population, no other sources –Microdata Two level of analysis: –Typology of houses supplied in rent –Typology of rent house demanders Cluster and factorial analysis Including information about mobility for labour reasons June,

Conclusions Micro rent market in Spain but with relevance in capitals Most associated to mobility needs for labour purposes –Direct and possitive relationship statistically significant Rent houses not statistically distinct from owned homes … Good quality.. Similar conditions Smaller than the owned stock June,

Conclusions Relevant to cover immigrants’ demand –From all income ranges Not homogeneous for Spanish households There is no a ‘Rent typology’ for Spanish households Main role for single households, both foreign as well as Spanish Not relevant for traditional households… ownership rate June,

Conclusions Very relevant in some provinces/cities, with rent rates similar to the international ones –Ceuta, Melilla, Canary Islands, Baleares, Barcelona, Cádiz, León, Coruña… Limitations in housing mkt in other: –Madrid, Housing needs has been covered by ownership June,

Future Rent market in Spain changes during 2000’s June,