Appraisal of Apartments in Albania using Hedonic Regression

INES NURJA

University of New York, Tirana,

ALBANIA

FATMA JAUPI

University of Tirana, Tirana,

ALBANIA

OGERTA ELEZAJ

Birmingham City University, Birmingham

UNITED KINGDOM

Abstract: - Valuation of property is an important part of the land organization framework since it has a direct

impact on people’s lives. The adjustments within the property estimation can dramatically change the

abundance of the organizations and their ability for development. A variety of people may benefit from a better

property valuation framework in our society because it reduces the risk of investing in this division and

encourages lower interest rates on loans. The aim of this paper is to empirically demonstrate that, aside from

location and address of properties, there are other major factors that influence the price of flats in Albania by

developing a hedonistic pricing model. The capital city of Tirana and also Durres were selected for this

research paper in order to develop a hedonic price model based on the data collected by properties sold at one

of the largest real estate agencies, “Century 21” in the country. According to the findings, apartment attributes

such as area of living, number of bedrooms and other factors influence the price. The findings also revealed the

marginal influence of the number of apartment spaces, which was dependent on the living area of the

apartment. This is the first empirical study to present the results obtained from a hedonic regression built using

data pertaining to Albania.

Key-Words: - Hedonic regression, appraisal of apartments.

Received: June 20, 2022. Revised: September 14, 2022. Accepted: October 10, 2022. Published: November 14, 2022.

1 Introduction

The real estate sector is an extremely critical part of

the national economy of a country and firmly related

with different sectors of the economy, confirming

the amount of development. When we mention the

housing market, the word is not only about the

property but also about the complementary sectors

that this market creates. The electrical services,

construction of water, shopping centers and other

community services are some of the sectors that

successfully generate additional employment and

financial gain. Focusing on the housing market as a

capital asset, it helps in the development of business

enterprises, providing stable long run rental income

and also acts as a credit collateral for business

endeavors.

The housing market, however, is distinct from other

consumer commodities in that it demonstrates

durability, variety, and geographical fixity.

According to the hedonic price model, products are

generally marketed as a bundle of intrinsic

characteristics, [1].

Since the real estate market is a significant sector in

a national economy and also affects different other

sectors in it, the research for modelling housing

pricing turns into a very important matter. As one of

the components of the land organization framework,

a productive real estate valuation system ensures

clarity and consistent (symmetrical) distribution of

data on the price of the property, [2].

The adjustment of the property estimation may

adequately influence the abundance of organizations

and their ability for development. Progression of

real estate valuation system improvement may be

helpful to various interest groups in the community,

in such a way that may lessen the risk of investment

in the housing market and furthermore support

lower rates of interest on loans, [3].

From all studies done until now about hedonic

appraisal of apartments in Tirana only, there is no

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record on attributes of sold properties and their

position cannot be distinguished by a geographic

data framework, [15].

The objective of this paper is to empirically

demonstrate that, aside from location and address of

properties, there are other major factors that

influence the price of flats in Tirana and Durres by

developing a hedonistic pricing model.

2 Literature Reviews

The hedonic price method, or hedonic regression,

recognizes that heterogeneous commodities can be

specified by their attributes or characteristics. The

model is used for estimating the value of this good

or the demand for a good, which indirectly affects

its market price.

Even though most of the scholars agree that it was

the Court, [31] who first used the hedonic price

model (HPM) to decide the hedonic price index of

cars, there is no consensus among them as to who

first introduced this model. Haas, [32] and Wallace,

[33] first displayed the model with the evaluation of

agricultural land. One reason to consider the Court’s

study as a substantial contribution is that it deals

with problems of nonlinearity and with changes in

underlying goods bundles, [5].

Robert and Shapiro, [6], are two other scholars that

contended about the Court’s methodology, by

stating that “implicit price components for each of a

bundle of product characteristics are established by

a regression operation that phrases the price of a

product as a function of the coefficients associated

with each characteristic. The price of a new product

(or different product can then be compared with that

of the previously existing product when one utilizes

these coefficients…”

There is also a theory stated by Colwell and

Dilmore in [7] which shows that Haas conveyed a

hedonic study more than fifteen years prior to Court,

even though he never used the term ‘hedonic’. Haas

analyzed price/acre adjusted for year of sale, road

type and city size, using data on 160 sales

transactions gathered from farm sales in Minnesota.

While the depreciated cost of building per acre, land

classification index, soil productivity index and

distance to the city center were included as

independent variables. There are also many other

scholars who contributed to the HPM over the years.

Authors in [8] raised the HPM by further developing

Court’s study. His work imprinted technological

change and novelty into hedonic prices through

quality of goods. Griliches, [9], worked on

automobile price indices using automobile models

as a unit of analysis, and his study attracted

substantial consideration, [10], [11].

Lancaster’s consumer theory and Rosen’s model are

considered important contributions to the

development of HPM. In [12] the authors

characterized the idea of utility of the items through

the value of their extraordinary qualities,

considering that a linear relationship. Their review

depends on the hypothesis of utilization, under

which the demand for a heterogeneous item, for

example, real estate, relies upon its attributes.

According to this research, the Lancastrian index is

more appropriate for consumer goods. According to

authors in [13], properties are heterogeneous and the

absolute value is made up of the total of each

property’s characteristic value, implying that HPM

ought to be nonlinear. Rosen’s model looks

appealing to estimate demand for durable goods.

Rosen's model is divided into two stages. By

relapsing the cost of a product on its qualities, the

first stage estimates the marginal price for the

attribute of interest. The first stage establishes a

price measure but does not show the inverse demand

function immediately. The inverse demand curve,

also known as the marginal willingness to pay

function, is generated from the implicit pricing

function calculated in the first step of estimation,

[14].

Because Rosen factored income into the consumer's

budget constraint, as income rises, so does the

consumer's marginal willingness to pay for a

specific implicit feature. The buyer's demand or

desire to pay for a characteristic is considered to be

a function of the buyer's utility level, wealth, and

other variables that impact tastes and preferences,

such as age, education, and so on. Rosen believes

that utilizing the marginal price as an endogenous

variable in the second-stage synchronous condition

may be used to estimate the inverse demand

function, which considers changes in revenues and

utility levels. If the inverse demand function can be

traced back utilizing the implicit marginal price

function, the utility change with regard to specific

quality modifications may likewise be calculated by

coordinating the inverse demand function, [14].

Bartik, [30], disagreed with Rosen's method for

estimating the hedonic price model, claiming that

the hedonic estimation issue is not the consequence

of demand-supply interaction, because individual

consumers cannot influence providers.

Because of the diverse environment in which the

model takes place, determination of housing

characteristics in the hedonic model is different in

various countries. The existence of a lift, a garage or

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air conditioning creates a favorable link with the

value of the house.

The house value is also positively affected by the

view; trees or park, [16] or if the property is located

by the sea or river. It is negatively affected when the

house is located near a cemetery, [14].

In the Albanian literature, most of the studies on the

housing market are conducted mainly at the macro

level. Rebi in [27] tried to find the impact of

housing prices on mortgages; Bozdo in [26] on the

stability of the financial system, while authors in

[24] studied the factors that have an impact on the

construction sector in Albania.

Thanasi, in his study for the hedonic price model in

Tirana, using a database of 1421 apartments

obtained through the real estate magazine “Celesi”,

found that location was the factor which seemed to

affect mostly the housing prices, [4]. Asilkan, [25]

used a sample of 200 properties in Tirana, taking

into consideration factors of location, structure and

neighborhood. Shehu in [28] brought up the result

that the number of rooms, property conditions and

balconies affect house prices, but the prices are

affected negatively if the apartment is located away

from the city center. According to the age of the

building, Shehu and Afezolli [29], on another study,

using data on some office buildings in Tirana, found

that, by remaining constant all the other factors, the

effect of the age of the building on the price of the

apartments in it is very small, only 0.3% if the

building is one-year-older, [17].

3 Hedonic Pricing Models

Explanatory Variables

The application of the hedonic price model to the

real estate market is predicated on a number of key

assumptions. Real estate products should be viewed

as heterogeneous since they can be distinguished

based on locational, structural or neighborhood

characteristics, as well as other factors such as the

type of the property.

One other underlying assumption is that the market

is perfectly competitive with a large number of

buyers and sellers. This statement is supported by

the fact that there are a large number of purchasers

looking for houses in the market, as well as a large

number of housing developers. As a result, no single

buyer or seller can obtain a major influence on the

price of the properties since individual unit

purchases or sales make up such a small part of the

market.

Although one may argue that complete knowledge

is hard to obtain in reality, the premise that

purchasers and dealers have the ideal data about real

estate product and pricing is fair. Purchasing a home

necessitates a significant financial investment. As a

result, buyers will try to gather as much information

as possible about the features of the units they want

before making a purchase. The majority of the

pertinent information, such as availability of the real

estate unit, its price, and its characteristics, may be

found in newspapers or received from brokers and

real estate agents. Suppliers, on the other hand, may

enhance their profitability and utility by having a

complete understanding of their core business and

market pricing. Such complete information, on the

other hand, may never be fully achieved in practice.

Lastly, the hedonic pricing model only works if

market equilibrium is assumed, and there are no

interrelationships between the implicit costs of

characteristics in the model, [18]. Because of the

flaws in the real-world property market, market

equilibrium is implausible. The assumption that the

price vector would adapt instantly to changes in

demand or supply at any point in time is ideological.

The idea that there are no interrelationships between

attribute implicit prices is likewise false since it

suggests that an attribute’s implicit price does not

change across all regions and property types.

Despite these questionable assumptions, which

require significant simplification and abstraction

from a complicated reality, the hedonic price model

has been widely used in housing market analysis,

[19].

The data may be insufficient; variables are

measured with error; and empirical variable

definitions are rarely exact, as Freeman correctly

remarked, but this does not render the approach

useless for empirical purposes.

Its major benefit is that it just requires a few pieces

of information, such as the property price, the

composition of dwelling characteristics, and a

thorough explanation of functional connections. The

marginal attribute prices are determined by

calculating the hedonic pricing function’s

parameters.

Authors in [20] are recognized as being the first to

use the hedonic price method in residential real

estate. They examined the connection between air

quality and property prices, but there was Freeman,

[21] who was credited as being the first to provide a

theoretical justification for employing this approach

in housing. He utilized the hedonic pricing equation

to calculate marginal implicit prices and willingness

to pay for property characteristics like

environmental quality.

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Residential properties are multifaceted commodities

with characteristics such as durability, structural

rigidity and geographical fixity, [22].

Housing characteristics are usually divided into

three categories; locational attributes, structural

features and neighborhood qualities. These

characteristics include both quantitative and

qualitative characteristics, [23]. As a result, the

property’s market values may be stated as a function

of its location, structure and neighborhood. The

implicit price of each home characteristic may be

calculated from the regression results, ceteris

paribus.

3.1 Albania Housing Market History

The real estate market is an open market, easily

accessible to anyone with enough liquidity to

become the owner of a real estate property. Like in

every sector of the economy, the development of the

real estate market is cyclical and goes through

several stages. Although there is no safe manner to

predict its development, being aware of the general

market’s trends is important to make a smart

investment in real estate. History has discovered that

the past and the present can tell so much about the

coming future.

While in the communism time frame, the state has

made the reallocation of the houses to the

individuals. With the breaking down of the

cooperatives, the rural area dealt with a few

problems after the 90’s. Being said, an internal

migration started to take place in Albania. People

were moving from the rural areas toward the

metropole Tirana. Because of this segment structure

change, there was an increase in the demand for

houses as well as an increase in the prices.

Recovery is the renaissance of the real estate market

cycle. In Albania this phase coincides with years

1998-2000, which is the period directly after the

recession. Year 1997 in Albania is the year of the

crisis, which directly affected the demand for houses

by decreasing it. Devaluation of the local currency

also brought a devaluation in the real estate market.

Being said that, in these two years, the real estate

market in Albania was trying to reach the balances.

The levels of construction until 2000 are considered

to be the lowest ones in the 20 years old history of

development of the real estate market. In 1998,

more than 778,000 square meter new surfaces were

built.

After the ’97 crisis, Albania has experienced high

rates of economic growth. In 2000, the invested

funds in real estate increased with 89,1% in

comparison with 1997. This boom was also

accompanied with optimistic hope for the future

prices of the apartments, which also brought an

increase in loans in Albania. In 2005, there was an

increase in loans to buy houses, with 74% or in

value 52 mil ALL.

The prices of apartments were increasing until 2007,

when it was first shown a slight decrease in the real

estate sector. Also, a decrease of 13,7% in sales

volume of apartments was shown. The decrease in

the new building being constructed showed that the

market was facing a decrease in the demand for real

estates, which eventually brought a slight decrease

also in the prices of the apartments.

4 Methodology

The database includes 4,009 apartments sold in

Tirana and Durres real estate market for the period

from 2015 until May 2021. The collected data

include the status of the property, defining it as new

or used; the year when it was sold; the city where it

is located; does it has or not an elevator; the price of

the apartment; the interior area; the gross area;

square meters of the common area; the number of

bedrooms; the number of balconies, the number of

toilets and the number of the floor of the apartment.

Even though the data collection was carefully made,

there are missing values at some of the variables

taken under consideration.

4.1 Hypothesis

The paper’s theoretical foundation is built on

describing the factors that influence the price of the

apartment. This hypothesis is based on the

information presented above, more specifically the

dependent variable price and the independent

variables year sold, common area, status, other

rooms, city, balcony, bathrooms and bedrooms. The

other factors such as elevator, furnished or

unfurnished, number of floors of the building and

number of floors of the apartment, land area and

gross area, kitchen, and living room are not included

in the regression since they were not important.

Multiple linear regression is used for this analysis

between the dependent variable and the other

independent variables. This form of regression

permits the evaluation of the relationship’s strength

between the sold price of the apartments as a

dependent variable and the other variables as

independent ones. Below, the hypothesis are

described:

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Null hypothesis: The factors taken into

consideration (status, city, year sold, bedrooms,

bathrooms, other rooms, common area, balcony,

interior area) do not affect the price of the

apartments sold.

Alternative hypothesis: Some of these factors or all

of them (status, city, year sold, bedrooms,

bathrooms, other rooms, common area, balcony,

interior area) affect the price of the apartments sold.

4.2 Regression Model

The below equation is constructed in order to

explain the regression:

Logprice = 𝛼0+ 𝛼1ST + 𝛼2CT + 𝛼3BED +

𝛼4BATH +𝛼5OTHER+ 𝛼6COMM + 𝛼7YEAR

+ 𝛼8logBAL + 𝛼9log INT (1) where:

Logprie - price of the apartments sold, denoted in

EUR, which is taken into logarithm in order to

interpret in percentage terms;

ST - the status of the apartment; new, used or in

project;

CT - the city Tirana or Durres, denoted as 1 and 0 in

the regression;

BED - the number of Bedrooms;

BATH - the number of Bathrooms;

OTHER - the number of Other rooms ( storage,

security, laundry, entertainment);

COMM - the common area;

YEAR - the year when the apartment is sold, from

2015 until 2021;

LogBAL - the balcony area taken as logarithmic so

that the interpretation is done in percentage terms;

LogINT - the interior area taken as logarithmic so

that the interpretation is done in percentage terms;

5 Results

Table 1.Descriptive Statistics

Min

Max

Mean

Std. Dev.

Price

10500

98376

76637

Interior Area

430

90.09

34.385

"Gros Area"

540

100

42.714

Balcony Area

313

1.55

11.820

Bedroom

1.82

.684

Bathroom

1.38

.522

Other Rooms

.14

.471

From table 1, the price, which is the dependent

variable, has an average value of 98,376 EUR. The

prices of the apartments vary from 10,500 EUR to

1,050,000 EUR. For price, we have a very large

standard deviation since the difference between the

minimum and maximum values are very big,

leading us to think that these data may not follow

the normal distribution, while for the common area

the standard deviation goes near to the mean. We

have to mention that the outliers affect the standard

deviation as well as the mean. We have tried to

remove the outliers from our database. As for the

independent variable of interior area, the descriptive

statistics shows the minimum value 12 square

meters and the maximum value 430 square meters.

The mean interior area is 90 square meters with a

standard deviation of 34.39 square meters.

After completing the descriptive analysis of the

variables taken into consideration, the regression

model is done. This multiple linear regression is

executed with the help of SPSS and the results are

summarized in the following tables.

The R value represents the simple correlation and

for the current data is 0.825 which indicates a high

degree of correlation.

Table 2. Model Summary

Model

Square

Adjusted R

Square

Std. Error of

the Estimate

.825a

.681

.662

.15644

So, 68.1% of the price can be explained from the

city where it is located, the year it is sold, the

number of bedrooms, bathrooms and other rooms,

the square meters of interior, common area and

balcony and finally the status of the apartment.

Table 3. ANOVA table

Model

Sum of

Squares

Mean

Square

Sig.

Regressi

7.695

.855

34.934

.000b

Residual

3.598

147

.024

Total

11.292

156

This indicates the statistical significance of the

regression model that was studied. Here, p<0.0001,

which is smaller than 0.01 and indicates that,

overall, the regression model statistically

significantly predicts the price variable.

Table 4 provides us with the necessary information

to predict the price (dependent variable) from the

city where it is located, the year it is sold, the

number of bedrooms, bathrooms and other rooms,

the square meters of interior, common area and

balcony and finally the status of the apartment. But

also, it shows us whether these independent

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variables contribute statistically significantly to the

model. (p are less than 0,0001 which is less than

0.01). Only for this research paper purpose, is

chosen a 90% confidence interval, so the

significance level remains 0.1. So, if we choose to

accept or reject the null hypothesis, we compare this

significance level 0.1 with the p-value of the

regression. The best alternative would be to reject

the null hypothesis and to accept the alternative

hypothesis which says that the independent

variables of our study have an important impact on

the price of the apartment sold.

Table 4. Regression Coefficients

Model

Unstandardize

d Coefficients

Standar

dized

Coeffic

ients

Sig.

Std.

Error

Beta

(Constant)

3.014

.192

15.719

.000

Status

.036

.020

.089

1.834

.069

City

.068

.032

.108

2.136

.034

Bedrooms

.028

.024

.091

1.189

.236

Bathrooms

.066

.030

.160

2.200

.029

Other

Rooms

-.076

.040

-.096

-1.902

.059

Common

Area

-.002

.002

-.066

-1.402

.163

Year Sold

.044

.026

.081

1.667

.098

logBalcony

Area

.066

.024

.141

2.748

.007

logInterio

Area

.857

.111

.560

7.742

.000

As we can observe from table 4, the status has a

p-value of 0.069 which is less than the

significance level of 0.1 and 0.05, meaning that

this variable is statistically significant. In other

words, the status is a very important factor in

the determination of the price of the apartment.

The positive coefficient of 0.036 indicates the

positive correlation between the price and the

status, explaining that, the newest the

apartment, the greater is the price. City is also

statistically significant in our study. This factor

has a p-value of 0.034 which is less than the

significance level of 0.1. In other words, the

status is a very important factor in the

determination of the price of the apartment.

Halvorsen and Palmquist have found another

way to obtain the semi elasticity for a dummy

regression by firstly taking the antilog of the

differential intercept. Subtract it from 1 and

multiply it by 100. The median price for

apartments sold in Tirana is 17% higher than

the median price of the apartments sold in

Durres. Based on the obtained results, 72,3% of

the apartments are sold in Tirana while only

27.7% are sold in Durres.

The number of bathrooms is a very important

factor in the determination of the price of the

apartment. The positive coefficient of 0.028

indicates the positive correlation between the

price and the number of bathrooms, explaining

that, for 1 unit (bathroom) increase, the price is

increased with 0.028%. Other rooms and the

Common Area have a negative impact. People

want more bedrooms or more space in the

living room/kitchen rather than the rooms for

laundry or storage.

Year sold is statistically significant since it has

a p-value of 0.098 which is less than the

significance level of 0.1. The positive

coefficient of 0.044 indicates the positive

correlation between the price of the apartment

and the year it is sold.

Also, as we can observe from table 4, the

balcony area has a p-value of 0.007 which is

less than the significance level of 0.1. This

means that this factor is statistically significant.

The positive coefficient of 0.066 indicates the

positive correlation between the price of the

apartment and the balcony area.

Interior area is statistically significant since it

has a p-value 0.000 which is less than 0.1. The

positive coefficient of 0.857 indicates the

positive correlation between the price of the

apartment and the interior area. If the interior

area is increased by 1%, the price of the

apartment is increased by 0.857%.

6 Conclusion and Discussion

The hedonic pricing model is a scientific

instrument that can be highly beneficial. With

enough and right data, we can evaluate each

effect of various property characteristics on

housing prices by using this method. Being

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said that, the hedonic pricing model is the most

common scientific approach for observing the

impact of one or more housing characteristics

on housing prices while keeping all other

variables constant. That makes it possible to

comprehend the behaviour of agents in the

housing market as well as showing how the

market itself functions.

At the end of this study, is it empirically proven

that the price of the apartments in Tirana and

Durres are affected by factor such as status of

the apartment, city, number of bathrooms,

bedrooms, other rooms, year when it is sold, the

common and interior area and the area of the

balconies.

Literature showed that one of the most

important factors was the location or the zone

of the apartment. In my study the geographic

space is not included since this is a different

study that is planned to be developed at another

moment. R square equal to 68.1% is a very

satisfying result, because 31.9% of the price is

clearly explained only by the location.

Being said that, the model in this study needs to

be improved.

As a future work, we aim to include the

geographic position (not how much away from

the centre, but to divide Tirana into preferred

and no preferred zones) by switching into a

spatial hedonic model. Later on, the model will

expand also in other cities, where the demand

for apartments will be increasing.

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