=Paper=
{{Paper
|id=Vol-3723/paper13
|storemode=property
|title=Information system for real estate trading operations based on the data analysis
|pdfUrl=https://ceur-ws.org/Vol-3723/paper13.pdf
|volume=Vol-3723
|authors=Oleh Veres,Pavlo Ilchuk,Olha Kots
|dblpUrl=https://dblp.org/rec/conf/modast/VeresIK24
}}
==Information system for real estate trading operations based on the data analysis==
https://ceur-ws.org/Vol-3723/paper13.pdf
Information system for real estate trading operations based
on the data analysis
Oleh Veres1,†, Pavlo Ilchuk1,† and Olha Kots1,∗,†
1 Lviv Polytechnic National University, Stepana Bandery str. 12, Lviv, 79013, Ukraine
Abstract
The process of buying and selling property heavily depends on accurate real estate valuation. We
have carefully studied the existing programs with the help of which we carry out real estate
transactions, described their features, advantages and disadvantages. Traditionally, real estate
valuation relied mainly on manual data analysis and subjective estimates, often resulting in
mistakes and delays. Implementing machine learning algorithms has shown to be effective in
addressing this issue, offering several benefits over manual assessments: high accuracy,
elimination of subjectivity and bias, time efficiency, cost reduction, utilization of geospatial data,
and well-supported results. The process of creating a machine learning model is conventionally
divided into four stages. Linear regression, decision tree, nearest neighbor, support vector, and
random forest algorithms were tested using standard parameters. The R-squared coefficient of
determination was chosen as the main metric. After comparing the coefficient of determination
of the results, it became clear that the "random forest" algorithm showed the best result. Using
manual hyperparameter selection for this algorithm, the mean absolute error of the predicted
value is 8.49%, with a median error of 1.9%. The built model meets the established quality
requirements and is ready for implementation in the information system for forecasting the value
of real estate. The system was divided into three separate services, each responsible for a specific
set of functions. The purpose of each service is outlined, along with their main functions and the
connections between them. Modular and end-to-end testing of the server and user parts of the
program was conducted to confirm the readiness of the system for use. All services function
properly and interact seamlessly with each other.
Keywords
Data Analysis, Machine Learning, Real Estate, Trading, Evaluation, Information System 1
1. Introduction
Real estate transactions involve significant amounts of money, and thading decisions need
to be made on the basis of relevant data. Correct valuation of real estate plays a crucial role
in the trading process. The value is determined by various factors, such as location, kitchen
and room area, condition, year of construction, amenities, nearby infrastructure,
neighborhood development trends, market trends, and many others. Overpriced properties
MoDaST-2024: 6th International Workshop on Modern Data Science Technologies, May, 31 - June, 1, 2024, Lviv-
Shatsk, Ukraine
∗ Corresponding author.
† These authors contributed equally.
oleh.m.veres@lpnu.ua (O. Veres); pavlo.g.ilchuk@lpnu.ua (P. Ilchuk); olha.o.kots@lpnu.ua (O. Kots)
0000-0001-9149-4752 (O. Veres); 0000-0003-4636-2309 (P. Ilchuk); 0000-0001-7123-3635 (O. Kots)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
can linger on the market without attracting buyers, while undervalued properties can result
in substantial losses for the seller. Accurate valuation is crucial for making informed
decisions and preventing financial losses [1].
However, the cost of hiring specialists for evaluating real property can be substantial.
Additionally, human limitations should be considered, especially when dealing with large
volumes of data. It's important to remember that the real estate search process can be time-
consuming, and sometimes the need to find a property can be urgent. Therefore, the task of
developing an information system that will help meet the needs of both sellers and buyers
is urgent.
The implementation of this system will simplify and speed up the process of finding the
best property for purchase, as well as the process of its valuation for further sale. Real estate
sellers will be able to quickly assess the value of their property depending on the
parameters, location, and current market conditions, and buyers will be able to receive a
list of recommended properties that are priced according to their real characteristics or
below market value.
Traditionally, real estate valuation has relied heavily on manual data analysis and
subjective estimates, frequently leading to inaccuracies and delays. Therefore, the use of
machine learning methods to predict the value of real estate objects is relevant, as it will
ensure transparency in the real estate market, reduce the cost of realtors and real estate
agents, and enable users to make more efficient decisions regarding the purchase or sale.
2. Analysis of recent research and publications
Utilizing machine learning to forecast real estate values has introduced unparalleled levels
of precision, productivity, and transparency. There's no longer a necessity to solely depend
on the expertise of industry professionals, who often grappled with vast data sets and
intricate criteria when establishing real estate prices.
The real estate market dynamics demand real-time adaptability, a task at which artificial
intelligence excels. Conventional valuation methods frequently rely on outdated data and
struggle to accommodate rapidly changing market conditions [5, 6]. Conversely, artificial
intelligence effortlessly integrates real-time changes, giving it a distinct advantage [2].
Consequently, the value estimates generated not only draw from historical data but also
accurately reflect the present market conditions, aiding stakeholders in comprehending the
constantly evolving landscape. The ability for real-time adaptation distinguishes artificial
intelligence from traditional approaches and highlights its crucial role in navigating a
rapidly evolving market, showcasing its revolutionary potential [3].
3. Benefits of employing artificial intelligence in real estate valuation
Enhanced Precision. The use of artificial intelligence tools helps to avoid subjectivity and
bias in determining the value of real estate.
Time Efficiency. Time is a precious resource in property valuation. Traditional property
valuation methods often demand days or weeks to produce comprehensive assessments,
whereas AI-driven processes excel in efficiency, yielding results swiftly. The time required
for real estate valuation is significantly reduced when data is analyzed by an AI system. This
accelerates transactions, introduces flexibility into the valuation process, and enables
stakeholders to promptly address shifting market dynamics.
Cost Reduction. Artificial intelligence's efficacy also impacts the financial aspect of
property valuation. Leveraging machine learning techniques diminishes the necessity for
repeated valuations by efficiently identifying trends from various data sources and
synthesizing them. This significantly reduces costs for both buyers and sellers.
Integration of Geodata. Geospatial data plays a crucial role in real estate valuation,
encompassing factors such as a property's proximity to amenities, location within flood
zones, or adjacency to industrial areas. Artificial intelligence algorithms can seamlessly
incorporate geographic information systems to include these variables in real-time
valuations, providing a level of detail that was previously challenging to attain. [4].
Clear understanding. The openness of valuations ensures transparency in real estate
transactions. The algorithms employed in AI-based valuations are not "black boxes"; they
provide clear justifications for the conclusions. Stakeholders are provided with a detailed
explanation of each valuation methodology. This transparency fosters trust and facilitates
well-informed decision-making.
Enhanced investment decisions. The empirical foundation of AI-powered valuation
furnishes stakeholders with a potent tool to refine investment choices. Investors, buyers,
and sellers leverage the resultant real estate valuation data to make more informed
decisions. The accuracy of valuation provided by artificial intelligence mitigates risks in
determining expected returns on real estate and analyzing market trends for strategic
decision-making. This approach prevents the potential of overpaying for a purchase or
underestimating the value of real estate, resulting in more successful and informed
outcomes [2].
3.1. Comparison of current systems for real estate sales, purchases, and leases
The study focused on analyzing the most popular applications for real estate sales,
purchases, and rentals in the Ukrainian market, specifically DIM.RIA [7] and OLX [8]. These
services boast a considerable advantage due to their popularity in Ukraine, resulting in vast
and expanding repositories of real estate data. Leveraging this data in service development
is feasible, as it can be acquired internally. Having thoroughly researched the most popular
applications used abroad (Zillow [9], Realtor [10], Redfin [11], PropStream [12]), the
comparative table (Table 1) is provided to emphasize their capabilities and compare them
with the system under development.
Of all the services, PropStream is the most similar in terms of functionality, as it provides
a wide range of data analysis features. However, it has a complicated user interface.
The key distinction between the system under development and existing solutions lies
in the approach to real estate valuation. While existing solutions primarily rely on basic
physical property characteristics (such as area, number of rooms, floor, year of
construction, availability of parking spaces, etc.), the system being developed will also factor
in location.
Table 1
Comparative analysis of the system with competitive applications
Applications
OLX DIM.RIA Zillow Realtor Redfin PropStream System
Features
under
development
Evaluation of real - + + + (paid) + + +
estate by its
parameters for the
seller
Evaluation of real - Partly + + - + +
estate by its
parameters for the
buyer
Real estate inspection - + (paid) + + + + -
service (condition and
compliance with the
ad)
Real estate offer - + (paid) + + + + +
service according to
selected parameters
Analytical report - + (paid) - - + + -
service for real estate
in the selected region
Service of automatic - + + + + + +
notification of new ads
by selected filters
Reflection of factors - - Partly Partly Partly + +
that positively and
negatively affect the
value of real estate
Displaying important - + + + + + +
locations and
infrastructure near the
selected property
Simple and intuitive + + + + + - +
user interface
Availability of - - - - - + Partly
analytical tools for
investors
Displaying useful data - + + + + + +
about the area where
the property is located
The system will analyze the nearest and most significant locations within a radius of 500,
1000, and 3000 meters from the property, considering how they may positively or
negatively impact its value. Emphasis on geospatial data is one of the most important factors
in real estate valuation [4].
Existing applications in the Ukrainian market fall short of meeting all user needs, lacking
sufficient functionality for recommendations and analytics. Hence, it is imperative to
develop a new service that addresses these deficiencies, enabling users to make more
informed decisions regarding real estate transactions.
4. Creating a conceptual model
4.1. Modeling business processes and project requirements
The challenge in predicting real estate values stems from the scarcity of resources,
particularly the confidentiality surrounding real estate data. The current approach to real
estate transactions in Ukraine is complicated and even somewhat outdated [13, 14]. The
main problem is that buyers and sellers can choose one of two options.
The first is to do everything yourself. Stakeholders have to analyze the market on their
own, compare many properties with each other, determine the feasibility of buying or the
fair price for selling, while putting in a lot of effort and spending a lot of time.
The second approach is to delegate the task by using the services of appraisers and real
estate experts. This will greatly reduce the amount of effort and time spent and will likely
lead to a better result. However, such services are usually expensive.
The process of buying real estate is quite complex and potentially requires the
involvement of third parties. In this case, the system for buying and selling real estate serves
only as a data filtering tool and does not perform recommendation functions.
It should also be paid the attention to how long the process of searching for and
evaluating real estate takes, if you use the services of a realtor. If the buyer does not find an
option that meets his or her requirements and meets the expected budget, he or she will
have to contact the realtor again to defend his or her interests and explain the requirements.
And the realtor, in turn, should try to adapt to the client's requirements, while looking for
possible alternatives, each time assessing the correspondence between the ad and the
property value.
The "to-be" business process diagram (Figure 1), which includes the use of the
information system, shows that the decision-making process for purchasing real estate has
been greatly simplified and involves far fewer steps. After filtering the property by the
specified parameters, the buyer can choose the options he is interested in.
Thanks to the system, the user can do without the services of a real estate appraiser, so
he does not need to explain his needs, requirements and limitations to outsiders. This is
beneficial not only from an economic point of view, but also from a practical point of view,
since the buyer knows his own needs best. It is also important to note that in case of a
mismatch between desires and capabilities, it is easier for the buyer to compromise with
his requirements than if other people demanded it from him.
Now let’s draw up requirements for the developed system.
Figure 1: The "to-be" business process diagram
Business requirements. This information system aims to streamline and expedite the
process of identifying optimal real estate for purchase and evaluating its value for
subsequent sale. The implementation of the system in the real estate sector will be
advantageous for buyers, sellers, agents, and investors alike, streamlining their business
processes.
The implementation of the system yields several positive effects:
• Enhanced real estate search: the system will enable buyers to swiftly and
efficiently discover properties that align with their requirements and financial
capacities.
• Enhanced valuation accuracy: real estate sellers will have the capability to more
precisely determine the value of their property, facilitating the identification of
buyers and the negotiation of profitable deals.
• Reduced investment risk: Investors will utilize the system to analyze and select
properties with greater potential, minimizing their risk exposure.
• Positive impact on the real estate market: all stakeholders (buyers, sellers,
agents and investors) will receive tools that will help improve the efficiency of
the real estate market and facilitate interaction between them.
User requirements. The information system should provide for several users with
different roles, which, accordingly, will have a different list of capabilities and functions that
they can operate.
List of functions for unauthorized users:
1. Possibility of logging and registration.
2. Ability to view the property for sale in a grid view and on a map.
3. Ability to filter data depending on the specified parameters.
4. View detailed information about the selected property.
Unique features for authorized users:
1. All available unauthorized user functions.
2. The ability to enter data about your property.
3. Determine the value of the property and the cost of rent, depending on the input
parameters.
4. Ability to see the factors that most affect the valuation of your own property
(negative and positive).
5. The ability to view the user's real estate for sale in your personal account.
6. Ability to view real estate for sale by other users in the form of a table and on a map.
7. Ability to filter data depending on the specified parameters.
8. View the factors that have the greatest impact on the valuation of the selected
property (negative and positive).
9. Receive recommendations in your personal account, depending on the predefined
parameters.
10. Receive notifications in case of new recommendations for purchase.
Unique features for the user with the investor role:
1. All available functions of the authorized user.
2. Ability to view graphs of changes in property value over time.
3. Ability to receive analytical data at the level of regions, cities and city districts
4. Availability of a calculator that predicts the time required to pay off the property.
5. The ability to receive notifications in case of the appearance of recommended real
estate according to the specified filters.
It is also needed to implement an administration page. A user with administrator rights
should be able to change the roles of existing users, as well as add, edit, and delete all
existing system object entities.
Functional requirements. Below is a list of actions performed by this system:
• To access the system's functionality, users must be able to register and log in.
• The system should allow users to search for available properties based on
various criteria, such as price, location, property type and other parameters.
• The system should provide recommendations for real estate that meets the
users' requirements and criteria.
• The system should help users determine the value of real estate based on real
data and market analysis.
The objects of actions in such a system are system users: unauthorized user, authorized
user, investor and administrator, as well as real estate objects.
The main types of actions are: searching for real estate, filtering results, receiving
recommendations based on user-specified requirements, estimating the value, and
displaying the parameters that most influenced the value in the evaluation process.
Systems can be divided into interactive and automated according to the nature of their
operation. Interactive systems allow users to interact with the system by entering search
criteria, viewing recommendations, and analyzing the results. Automated systems are those
that analyze data and provide recommendations and cost estimates based on algorithms
and data.
Non-functional requirements. So, first, it is needed to define the business rules for
using the system:
• The system must comply with legal requirements and standards in the field of
real estate and data protection.
• The system must contain a link to the original source of the ad.
• The system should provide users with objective and accurate recommendations
on real estate and its value.
The next step is to list the most important quality attributes that need to be ensured for
correct operation of the system:
• Efficiency - the system must work quickly and efficiently, even with a large
volume of data and requests.
• Flexibility - the system should be open to expansion to allow for future
implementation of new features and capabilities.
• Scalability - the system must be able to scale to handle growing volumes of data
and users.
• Security - the system must provide an adequate level of protection for user data,
including confidential real estate information and personal data.
• Reliability - the ability to ensure uninterrupted operation of the system and the
ability to recover in the event of errors or failures.
• Convenience - the system should have a simple and intuitive user interface that
will allow you to use all the planned functions.
• Mobility - the system should be available on different devices and platforms.
To be able to realize high efficiency and scalability of the system, it is necessary to
consider the possibility of applying containerization of the system and the use of
orchestration tools. Although the system is not designed to be used by a large number of
users, it should have the potential for widespread use.
Another important component is the use of data caching functions, which will allow you
to efficiently process the same user requests, which are expected to be quite large.
4.2. Modeling objects within the subject area
Based on the program's tasks and functions, as well as the intended users and their
capabilities, a use case diagram was constructed (Figure 2). This diagram visualizes the user
and functional requirements of the system.
The diagram depicts four system actors: user (authorized), unauthorized user,
administrator and investor. Users can potentially elevate their role within the hierarchy to
access a broader range of functions if needed.
Figure 2: Use case diagram
User (or authorized user) – possesses the same functions as an unauthorized user but
with significantly expanded options. He has the ability to create, edit, and/or delete their
own real estate advertisements.
Unauthorized user – has the ability to view real estate for sale in both grid and map
formats, utilizing filtering tools by parameters. Additionally, they can access comprehensive
information about the real estate unit, including the contact details of the advertisement's
author.
Administrator – is tasked with managing all system entities, including the ability to
modify user roles. Upon completing the definition of system requirements, user roles,
functions, and capabilities, the next stage involves detailing the program implementation.
During this stage, the entities available in the system, along with their methods, attributes,
and relationships with each other, will be determined.
Investor – is a unique system role that extends the capabilities of an authorized user. It
provides access to analytics tools, allowing investors to view graphs, estimate the cost of
renting a real estate unit, and calculate the payback period through rent. Additionally,
investors can opt to receive notifications about recommended real estate according to
specified filters.
Let's build a class diagram (Figure 3) for the part of the software system responsible for
searching, filtering, modifying and analyzing data, as well as for training machine learning
models, testing them and applying them to solve real estate valuation problems.
Figure 3: Class diagram
The DataProvidingController class. Receiving and processing HTTP requests from
users. Class attributes: dataProcessor - an object of the DataProcessor class responsible for
working with data; mlModelProcessor - an object of the MlModelProcessor class
responsible for working with machine learning models. Class methods: processDataForCity
- download, process, and save data related to real estate for a given city; trainMLModel -
train a machine learning model based on the data available in the system;
predictRealEstatePrice - predict the price of real estate transferred as a parameter based on
the machine learning model available in the system.
The DataProcessor class. Collection, processing, unification and storage of data related
to real estate. Class attributes: dataCollector - an object of the DataCollector class
responsible for collecting real estate data; dataUnifier - an object of the DataUnifier class
responsible for unifying data; databaseDataSaver - an object of the DatabaseDataSaver class
responsible for saving data. Class methods: processDataForCity - collect real estate data
from various sources, process it, unify it and save it to the database.
The DataCollector class. An abstract class that is responsible for collecting real estate
data for a given city from a specific information source using specified filters. Class
attributes: dataSource - source of information or link to the resource; apiKey - key to use
the resource; cityName - name of the city to search for data; nonNullFields - list of required
fields for filtering. Class methods: collectData - an abstract method for collecting data from
a specific resource.
The OlxDataCollector class. Implementation of the abstract DataCollector class, which
is responsible for collecting real estate data from the OLX resource. It uses the data access
key and filters the data by city name and required fields. Class methods: collectData -
collecting real estate data from the OLX resource; saveDataIntoFile - a private method for
saving the received data as a .csv file; callApi - a private method for calling the OLX service
API (application program interface) to receive data by specified filters.
The DimRiaDataCollector class. Implementation of the abstract DataCollector class,
which is responsible for collecting real estate data from the DimRia resource. It uses the
data access key and filters the data by city name and required fields. Class methods:
collectData - collects real estate data from the DimRia resource; saveDataIntoFile - a private
method for saving the received data as a .csv file; callApi - a private method for calling the
DimRia service API (application program interface) to receive data by specified filters.
The DataUnifier class. Unification of data from different resources into a single format.
Also responsible for modifying and supplementing data related to the location of real estate.
Class attributes: addressExtractor - an object of the AddressExtractor class that is
responsible for converting the address of the property into a single format used in this
system; addressConvertor - an object of the AddressToCoordinatesConvertor class that is
responsible for obtaining geographic coordinates by the full address; geoDataFiller - an
object of the GeoDataFiller class that is responsible for searching and filling in data on
geolocation near a specific property. Class methods: unifyDataFromOlx - unification of data
received from the OLX resource; unifyDataFromDimRia - unification of data received from
the DimRia resource; readDataFromFile - a private method for reading data from a given
file; completeDataWithDetails - a private method for supplementing real estate data with
geodata.
Class AddressExtractor. Extract the address and bring it to a single format used in the
application. Class methods: extractAddressFromString - extract the address from the text
and bring it to a single format.
Class AddressToCoordinatesConvertor. Getting geographic coordinates of a location
by its full address using a third-party service. Class attributes: dataSource - a link to a third-
party API that converts the address to coordinates. Class methods: convert - getting the
coordinates of the location by its address.
The GeoDataFiller class. Search and fill in geolocation data near a specific property.
Class attributes: dataSource - a link to a third-party API that returns a list of locations near
the specified geographic coordinates; queryFor500MetersArea - a request to the API with
the search filter applied to search for locations within a radius of 500 meters;
queryFor1000MetersArea - a request to the API with the search filter applied to search for
locations within a radius of 1000 meters. Class methods: fillClosestLocationsToRealEstate -
search and fill in data on real estate objects with geodata; calculateCityCenterDistance -
calculate the distance from a given real estate location to the city center; callAri - a private
method for calling the API of a third-party service to search for locations near a given
location, using filters.
The DatabaseDataSaver class. Saving data from a .csv file to a database. Convert file
rows to entities of different tables. Class attributes: dataSourceFilePath - the full path of the
.csv file in the system. Class methods: saveDataToDatabase - saves data from a .cv file to a
database; extractFlatData - a private method that converts a .cv file string to the Flat
property table entity; extractFlatGeoData - a private method that converts a .csv file string
to the FlatGeoData property geodata table entity.
The RealEstateMLModelProcessor class. An abstract class for training and validating
a machine learning model, as well as predicting the value of a transferred real estate object.
Class attributes: city - the name of the city for which you want to train the machine learning
model. Class methods: trainMLModel - train the machine learning model based on the data
in the database; predictRealEstatePrice - predict the value of the transferred real estate
using the model; validateMLModel - validate the machine learning model.
The FlatMLModelProcessor class. Implementation of the abstract class
RealEstateMLModelProcessor to train and validate a machine learning model for the Flat
object, as well as to predict the value of the transferred real estate object. Class methods:
trainMLModel - train the machine learning model based on the data in the database;
validateMLModel - predict the value of the transferred real estate using the model;
predictRealEstatePrice - validate the machine learning model; saveMLModel - save the
trained model.
The HouseMLModelProcessor class. Implementation of the abstract class
RealEstateMLModelProcessor for training and validation of the machine learning model for
the House object, as well as prediction of the value of the transferred real estate object. Class
methods: trainMLModel - train the machine learning model based on the data in the
database; validateMLModel - predict the value of the transferred real estate using the
model; predictRealEstatePrice - validate the machine learning model; saveMLModel - save
the trained model.
The MLModelDataPreprocessor class. An abstract class responsible for processing
data used in machine learning models. Class methods: loadDataFromDB - load data from the
database; prepareData - prepare data before using it in the model.
The TrainingDataPreprocessor class. Implementation of the abstract class
MLModelDataPreprocessor, which is responsible for processing data used in the model
training process. Class methods: loadDataFromDB - load data from the database;
prepareData - prepare data before using it in the model training process.
The InferenceDataPreprocessor class. Implementation of the abstract class
MLModelDataPreprocessor, which is responsible for processing data used in the process of
making predictions of the initial data by the machine learning model. Class methods:
loadDataFromDB - load data from the database; prepareData - prepare data before using
them in the process of predicting the output data by the model.
The ApplicationConstants class. Saving constant values used in the application. Class
attributes: OLX_SOURCE - a link to the OLX resource API for obtaining real estate data;
OLX_API_KEY - a key to use the OLX resource; DIM_RIA_SOURCE - a link to the DimRia
resource API for obtaining real estate data; DIM_RIA_API_KEY - key to use the DimRia
resource; ADDRES_TO_COORDINATES_SOURCE - a link to a third-party API that converts
the address to coordinates; GEO_DATA_SOURCE - a link to a third-party API that returns a
list of locations near the specified geographic coordinates.
Here are the diagrams that explain how the process of buying and selling real estate
works for users of this system. Activity diagrams have been constructed that describe step
by step the process of buying (Figure 4) and selling (Figure 5) real estate when using the
system.
The most important operation in the system under development is the valuation of real
estate according to its parameters. Therefore, it is also advisable to consider the list of steps
that need to be taken to implement this process. Activity diagram of the subprocess
describing the real estate valuation operation is shown in Figure 6.
5. Developing a machine learning algorithm to address the task of
predicting real estate values
The aim of the information system is to streamline the real estate search, valuation, and
facilitate advantageous transactions between buyers and sellers. The primary objective of
the project is to implement machine learning algorithms to execute the valuation function,
catering to all participants in the real estate market. Simultaneously, attaining the utmost
accuracy in forecasting is crucial. The target is to reach an average accuracy of 5-7% in
determining real estate values. The error rate will be computed using test data. Achieving
this level of accuracy will be deemed satisfactory, as the average cost of realtor services
typically ranges from 3-5% of the total real estate selling price, thereby almost entirely
compensating for any errors in the calculations.
5.1. Stages of developing a machine learning model and rationalizing approaches
to address the issue
The creation of a machine learning model involves several stages, with the successful
completion of each preceding stage greatly influencing the subsequent ones, as well as the
overall final outcome.
The first stage:
• searching and downloading data from all available sources. For this purpose, the
official API of the source is used;
Figure 4: Activity diagram describing the process of buying real estate
• filtering data: finding and removing duplicate ads, as well as identifying outliers
- data objects that stand out too much from the rest;
• checking if there is any missing data in the ads and what kind of data. If missing
data is deemed relatively insignificant, it can be filled in; however, if it's deemed
crucial, such records should be filtered out. Filling in missing values can be done
using the mean (when the sample has a normal data distribution, no outliers or
abnormal values), mode (when the distribution is not normal and the sample
contains outliers), and median (to determine the category) of the total sample.
The choice of the optimal approach to averaging values depends on the type of
data and its distribution in the sample;
Figure 5: Activity diagram describing the process of selling real estate
Figure 6: Activity diagram of the subprocess describing the real estate valuation operation
• data unification: converting them to the form used in the system;
• determining the coordinates of real estate by address using an external API;
• supplement the geographical data of the property: The count of nearby bus stops,
hospitals, schools, and other amenities.
The second stage:
• identifying the minimal set of crucial features and parameters of real estate that
significantly impact a machine learning model's ability to forecast real estate
value. This process is conducted empirically, involving the modification of
certain individual real estate characteristics to enhance their processing
efficiency within the model. To achieve this, the One-Hot Encoding method is
employed, which simplifies complex categorical features by representing them
as binary values of 0 or 1.
The third stage:
• dividing data into two sets - training and testing, typically in an 80%-20% ratio.
The testing data is chosen using cross-validation to ensure robust evaluation of
the model's performance. The approach involves sequentially extracting 20% of
the data as test samples from the entire dataset. The model is then trained using
the same algorithm on the remaining data, with different subsets allocated for
testing: 0-20%, 20-40%, 40-60%, 60-80%, and 80-100%. This iterative process
ensures optimal training data selection and mitigates the risk of overfitting the
model;
• training of machine learning models for a large city typically involves dealing
with thousands to tens of thousands of active ads (prior to filtering). Given this
relatively small dataset size, the expected training time for the model should
ideally range from seconds to minutes.
The primary objective is to ascertain the value of real estate using specific input
parameters, a task for which regression algorithms are instrumental [15-28]. Regression
involves examining the connection between independent variables or features and a
dependent variable or outcome. In the context of machine learning, regression serves as a
method for predictive modeling, enabling the prediction or anticipation of results. In our
analysis, we explored a variety of algorithms commonly used for regression tasks. These
included linear regression [15-19], multiple linear regression (MLR) [20], decision trees
[19, 23, 24], random forests [19, 29, 30], support vector regression (SVR) [19], and K-
nearest neighbours (KNN) [31]. Regression techniques vary in their data handling
capabilities. Some can handle a high number of independent variables, while others are
better suited for specific data types. Machine learning regression models make varying
assumptions about the relationships between independent and dependent variables. To
achieve optimal results, avoid relying solely on a single algorithm. Instead, evaluate the
performance of multiple approaches to identify the most efficient option. To efficiently
compare different algorithms, we'll start with their default settings.
The fourth stage:
• evaluating model performance with an appropriate quality metric.
To analyze the impact of model complexity on accuracy, we will employ a regression
quality metric [32]. To ensure consistency, a single metric was selected to evaluate model
quality. After examining various popular metrics, R-squared was deemed the most suitable
choice for this task.
R-squared, also known as the coefficient of determination, quantifies the proportion of
variation in the dependent variable that can be attributed to the regression model. A key
feature of this approach is its ability to quantify the benefit of using the full model with
explanatory variables. This is achieved by comparing it to a simpler model that only predicts
a constant value (no influence of input variables) and the input variables are missing or
their regression coefficients are equal to zero.
A widely applicable formula exists for computing the coefficient of determination [34]:
∑𝑛𝑖=1(𝑦̂𝑖 − 𝑦𝑖 )2 (1)
𝑅2 = 1 − ,
∑𝑛𝑖=1(𝑦𝑖 − 𝑦𝑖 )2
where 𝑦𝑖 – actual value; 𝑦̂𝑖 – calculated by the model value; 𝑦𝑖 – average value:
1
𝑦𝑖 = 𝑛 ∑𝑛𝑖=1 𝑦𝑖 . (2)
R-squared (coefficient of determination) tells us how much improvement our regression
model brings over a simple model that just predicts the average value for all data points.
In practice, the interpretation of the R-squared value is guided by the following scale
[34]:
• R-squared values below 0.5 suggest a weak to moderate fit between the
predicted and actual values;
• R-squared values greater than 0.5 indicate a moderate to strong fit between the
predicted and actual values;
• An R-squared value exceeding 0.8 generally indicates a strong fit between the
predicted and actual values, suggesting the model explains a large proportion of
the variance.
It's evident from the formula that when the predicted values of 𝑦̂𝑖 equal the arithmetic
mean 𝑦𝑖 , the calculation in the fraction yields 1. Consequently, the coefficient of
determination equals 0, indicating the model's poor performance. Conversely, if the
predicted values 𝑦̂𝑖 equal the actual values 𝑦𝑖 , the numerator will be 0, resulting in a
coefficient of determination of 1. This signifies that the model accurately predicts all values
[34].
Once we've identified the most effective algorithms for a particular task, we proceed by
selecting one or more of them and fine-tuning their hyperparameters. This enables us to
attain a high level of accuracy tailored to the specific task at hand.
Certainly, in this approach, machine learning regression algorithms will be sourced from
pre-existing libraries, given that the outlined sequence of steps entails concurrent
utilization of multiple algorithms.
It was determined to gather all essential data solely for a single designated city due to
the resource-intensive nature of the data collection process. Moreover, individual machine
learning models must be trained for each city to ensure the program's accuracy. This
necessity arises from the significant variation in real estate prices across different cities in
Ukraine.
5.2. Selection and justification of tools for developing system architecture
components
After researching the problem and algorithms for solving it in detail, evaluating existing
competitive systems, determining the procedure for training a machine learning model,
building diagrams and charts that describe the application's operation, and establishing
requirements, you can finally proceed to the project development stage.
To facilitate the implementation of this system, a microservice architecture was chosen
for its ability to provide simplicity, flexibility, and scalability [35-38]. The software product
will comprise three distinct, interconnected services: the "Data providing service" the
"Backend service" and the "Frontend service".
Data providing service (Figure 7). This service will be dedicated solely to data
operations, including searching, filtering, and initial processing. Additionally, it will handle
model training, testing, and real estate value prediction based on input parameters. Its
responsibility also extends to regular data updates at specified intervals to monitor market
dynamics and furnish the application with current data, crucial for advertisements.
Figure 7: Scheme of operation of the "Data providing service" service
The data is stored in the database and is always available for other parts of the
application.
Python is the best programming language for this task, as it is quite flexible and has a
large number of mathematical libraries related to data processing and machine learning.
The following libraries are used:
• Pandas – a library used for data processing and analysis. It provides access to
data structures such as Series and DataFrame, as well as tools for working with
them. These data structures are built on top of the NumPy library, which is
another important library for numeric and array operations in Python.
• DataFrame – a two-dimensional labeled data structure that resembles a table.
This structure consists of rows and columns, where each column can have a
different type of data (for example, integers, floating point numbers, text).
DataFrames can be considered as a container for Series objects.
• Series – a one-dimensional labeled array that can contain data of any type. You
can work with it as a regular array, accessing data by index, as well as with an
associated array, accessing data by key.
• Pyspark ML – a library that serves as an interface for working with Apache Spark.
It provides access to machine learning algorithms, feature engineering tools, and
machine learning model evaluators designed to work flawlessly with distributed
data processing. It also includes a wide range of machine learning algorithms,
including regression, classification, clustering, and collaborative filtering.
• Scikit-learn – one of the most popular libraries used in programs that involve
working with machine learning. It contains tools for data processing,
dimensionality reduction, anomaly detection, and provides a wide range of
machine learning algorithms for classification and regression.
• OpenStreetMap Nominatim API – a no-cost service enabling retrieval of
geographical coordinates for a location on the map using its complete address,
as well as reverse geocoding, which provides the address information for a given
set of coordinate.
• Python Overpass API – a free service that allows you to interact with the
Overpass API service based on the OpenStreetMap project. It allows you to get
information about the location of various geographical objects, such as public
transport stops, parking lots, restaurants, buildings, etc. Using this service, you
can also build navigation routes, determine distances between locations, and
much more.
Backend service (Figure 8). The backbone of the application's business logic, this
component facilitates the transmission of data to the "Data providing service" using
parameters obtained from the user to ascertain property values. Additionally, it offers
diverse functionalities for data analysis, processing, real estate recommendations, email
notifications, data caching, and user authorization. Separating the logic in this way will give
the system flexibility, allow it to scale more efficiently, add new and maintain existing
functionality, and ensure simplicity. The development of this component leverages the Java
programming language and the Spring Boot framework.
Figure 8: The scheme of the "Backend service" service
This approach is used because it is popular for creating web servers and applications
used in microservice architecture. Its advantages are: quick creation and configuration of
applications; simple database management; use of dependency injection and control
inversion; a wide range of tools; advanced support and ease of testing; support for aspect-
oriented programming; a large community, which means easy resolution of potential
difficulties and problems [39].
Frontend service (Figure 9). This service will oversee the interaction between the
application and end-users. Its primary role is to visualize data using various methods,
present information in a user-friendly manner, and ensure intuitive navigation and usage
of the application. Also, this service should correctly receive and transmit data for
communication with other services. It will be implemented as a web application, as it allows
for easy support and distribution of the project, provides accessibility from any user device.
Figure 9: The "Frontend service" service working scheme
For the development of this component, HTML and CSS technologies were utilized
alongside the JavaScript programming language, supplemented by one of its leading
frameworks, Angular. This framework was chosen because of a number of its advantages
that will be useful in the development of this system [40]:
• The declarative approach to application development provides a clear and
organized way of representing the program structure and linking data, which can
significantly increase the speed of development.
• The use of modules as units of system components, which makes it possible to
easily maintain the code and reuse it. This is especially useful when the
application grows to a large size.
• Integration with TypeScript, which improves code quality, reduces errors, and
provides access to additional features. This makes it easier to maintain and
refactor the code.
• Angular's two-way data binding is a powerful feature that makes it easy to
synchronize data between the model and the view. It automatically updates the
user interface based on model changes and vice versa, which can save developers
a significant amount of time and effort.
• Extended testing capabilities due to the modularity of the application, as well as
the availability of special utilities. Provides quick creation of end-to-end, unit
tests, and integration tests.
6. Testing of the newly developed algorithm for forecasting real estate
values
6.1. Exploring, filtering and handling data
The initial and pivotal stage, setting the groundwork for all subsequent functionalities of
the system, involves acquiring pertinent real estate market data. The volume and quality of
data accessible for training and testing a machine learning model greatly influence its
efficacy. Data can be of different types and come from different sources, such as databases,
spreadsheets, or APIs [41].
It's essential to explore numerous sources of information to ensure an ample supply of
data for constructing a high-caliber machine learning model. Simultaneously, regularly
updating this data at specified intervals enhances the potential for in-depth analysis,
facilitates notification functionalities, and enables continual model retraining with current
data.
It was decided to collect all the necessary data only for one specific city (Kyiv), as the
data collection process is very resource-intensive, and especially because a separate
machine learning model needs to be trained for each city to ensure the program's accuracy
[42]. Consequently, developing a single universal model for the entirety of Ukraine would
likely fail to accurately estimate the cost of input data, resulting in unpredictable outcomes,
particularly for outliers. To address this, various approaches to data collection were
employed. The initial approach involved data mining through the official APIs of the
DIM.RIA [7] and OLX [8] websites. Additionally, Telegram channels such as "Real Estate Kyiv
and Region" [43] and "Real Estate of Kyiv Region" [44], which publish ads for property sales
and leases, were utilized. Using the Telethon library [45], this data was collected and
processed for subsequent utilization.
Upon data collection and processing, filtration becomes imperative. In cases where a
property listing lacked values for kitchen or living space, the median of these values within
the entire dataset was employed to populate the missing data. The arithmetic mean was
deemed inappropriate due to the skewed distribution of this data. Instead, for categorical
data types such as heating type, ad type, and wall type, the mode of the sample data was
utilized for filling in missing values.
The following data was determined to augment the real estate information in Kyiv:
kitchen area - 13 square meters, living area - 30 square meters, wall type - red brick, year
of construction - 2013, ad type - from an intermediary, heating type - centralized..
In order to translate the address of the property into coordinates on the map, we used
the open source OpenStreetMap Nominatim API [46]. We identify all significant locations
within a specified radius near the property by employing an open-source data tool, the
Python Overpass API [47]. Utilizing this API necessitates the creation of a suitable query
containing the coordinates of a specific point and the desired search radius.
We obtained 127 locations within a 100-meter radius of the address. Clearly, this data
will require filtration, as the list encompasses various items such as trees, trash cans, street
lamps, mailboxes, and benches. Nonetheless, among these, there are crucial locations that
are likely to influence the value of real estate.
6.2. A machine learning model training process
With the data collected, filtered, and enriched with supplementary parameters, the next
step involves initiating model training. It has been determined to employ multiple machine
learning regression algorithms simultaneously to ascertain the one most adept at tackling
the given task.
The algorithms were executed with default parameters, and the results were obtained
(Figure 10).
Figure 10: Comparison of model accuracy trained with different algorithms using the R-
squared metric
Upon comparing the coefficients of determination of the obtained results, it became
evident that the "random forest" algorithm outperformed the others. Consequently, further
model development and hyperparameter tuning will be conducted for this algorithm.
The subsequent hyperparameters were chosen empirically, yielding enhanced outcomes
in forecasting the predicted values via the random forest algorithm. Through
hyperparameter tuning and employing the cross-validation method, the R-squared value
was elevated to 0.81, signifying a commendable achievement.
To assess the machine learning model, a validation dataset of real estate data was
established and tested to evaluate the model's performance in predicting values against
actual data (Figure 11).
In Figure 11, the vertical axis depicts the real estate value per square meter in US dollars,
while the horizontal axis represents the proportion of the number of advertisements. The
red points indicate the predicted values by the model, whereas the blue ones represent the
actual values.
The graph illustrates a phenomenon known as heteroskedasticity, wherein as the
dependent variable increases, the predicted variable exhibits larger deviations from the
actual values. In other words, as the value of real estate per square meter increases, the
variability of the predicted values also increases. This issue stems from the limited amount
of data utilized during model training, which is a common occurrence.
Figure 11: The outcome of forecasting the value of real estate per square meter in Kyiv
based on its parameters using the developed model
The majority of properties in the dataset have a price per square meter of less than
$2,000, making it challenging for the model to accurately predict such unique cases. In the
future, addressing this problem can be achieved by gathering additional data on real estate
for sale.
To enhance the assessment of the absolute error calculation results, the data are
presented as follows:
APE > 50% for 0.012084592145015106 of test data
APE > 20% for 0.1933534743202417 of test data
APE > 10% for 0.31722054380664655 of test data
APE > 5% for 0.4108761329305136 of test data
APE > 1% for 0.5347432024169184 of test data
APE < 1% for 0.4652567975830816 of test data
Here, APE represents the absolute error determined by the formula:
𝑑𝑖 − 𝑑̂𝑖 (3)
𝐴𝑃𝐸 = | | ,
𝑑𝑖
where 𝑑𝑖 – real value; 𝑑̂𝑖 – calculated by the model value.
Figure 12 illustrates the percentage of absolute error in forecasting real estate values.
The average error stands at 8.49%, with a median of 1.9%.
In summary, regarding the machine learning model, it can be concluded that overall, the
model performed admirably, as indicated by the study results. With the consistent
accumulation of new data and subsequent retraining of the model, there is even the
prospect of achieving improved performance.
1%
18%
47%
13%
9%
12%
APE<1% 1%50%
Figure 12: The absolute discrepancy in the forecasted real estate worth
By adhering to the same methodology, it is feasible to devise an effective model for any
other city in Ukraine, provided there is a substantial volume of data available.
6.3. Evaluating and deploying microservices for application functionality
The subsequent stage involves testing the service serving as the system's server component.
To guarantee the utmost reliability of the service, unit tests were devised for all its
classes and methods. These tests encompass various scenarios for both correct and
incorrect requests, objects, parameters, and so forth. This not only ensures the system's
predictability but also serves as a crucial factor for its subsequent maintenance and
enhancement.
Additionally, we conducted manual testing of the service using the Swagger tool. We
validated the accuracy of all program endpoints by executing various requests with
different parameters. This testing process uncovered several minor errors, which were
subsequently rectified.
Once the server component has been confirmed to function correctly, the next step is to
commence testing the service responsible for the user aspect of the system. In this scenario,
conducting end-to-end testing of all functionalities and capabilities of the service is most
suitable. Upon successfully completing this testing stage, the system can be deemed tested
and prepared for use.
Given that the system comprises three distinct microservices necessitating configured
connections for information exchange and shared database usage, it's crucial to facilitate
swift and straightforward installation, configuration, and execution of the system.
Moreover, the services employ different technologies, such as Java, Node.js, and Python,
which may require additional setup, environment variable creation, and similar
adjustments.
The optimal solution to this challenge is to containerize each service separately using
Docker tools. This method not only streamlines the deployment process but also enables
scalability using orchestration tools. Similarly, the "Frontend service" and "Data providing
service" were containerized, generating the respective Docker images for each.
Next, we create a docker-compose.yaml file, enabling the configuration of essential
parameters and links between multiple containers.
With the latest versions of the images for all system services created and the
corresponding configuration file in place, execute the following commands to initiate the
system: "docker-compose build", and once completed, "docker-compose up".
Figure 13 depicts the interconnected containers of all individual services utilized in the
system via Docker Compose. The servers launched successfully, affirming the accuracy of
the configuration.
Figure 13: The outcome of system deployment via Docker Compose
Figures 14 and 15 depict the outcome of the successfully deployed system.
Figure 14: Real estate value estimate for the buyer
Figure 15: The outcome of real estate evaluation based on the parameters set in the system
It has been confirmed that all services operate correctly and interact seamlessly with one
another. Data is accurately transmitted, stored, and presented. This marks the concluding
stage of verification, confirming the system's readiness for utilization.
7. Conclusions
The study's outcome is a machine learning model capable of determining real estate value
based on its physical parameters and geographical location. The primary advantages of
employing artificial intelligence methods for predicting real estate value were analyzed.
We examined both Ukrainian and international applications addressing the challenge of
value forecasting and real estate valuation.
A distinctive feature of our development is that the model training process incorporates
not only basic real estate characteristics but also geospatial data. This includes considering
the presence and quantity of specific types of locations within a specified radius of the
property, thereby enhancing the accuracy of value predictions.
The process of constructing a machine learning model is conceptually divided into four
stages: data collection, filtering, processing, supplementation, partitioning into various
samples, and training the model based on these datasets. Regression methods, algorithms,
and quality metrics pertinent to predicting real estate value are explored. The model's
efficacy is assessed on a validation dataset, and the prediction results along with the
absolute error are visualized using graphs and charts. Analysis of this data leads to the
conclusion that the model's performance aligns with the system's specifications, thus
signifying the success of the study.
The developed information system will streamline and expedite the process of
discovering optimal real estate for purchase, as well as facilitating its assessment for future
sale. Real estate sellers will promptly evaluate their property's worth based on parameters,
location, and prevailing market conditions, while buyers will access a list of recommended
properties priced in line with their actual attributes or below market value.
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