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\chapter{Introduction}
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\section{Background}
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    The global real estate market shows positive growth trends despite various challenges.
Real estate market remains a key sector for people like homebuyers and investors,
influenced by complex characteristics with heterogeneous nature and affected by numerous elements like policies and consumer trends.
The market is projected to grow from \$4,143.71 billion in 2024 to \$4,466.58 billion in 2025, and global real estate investment volumes continue to increase in 2024\cite{JLL2025}.

Thailand's real estate market faces both challenges and opportunities in the current economic climate. The real estate market in Thailand,
especially in Bangkok in 2024-2025, faces challenges such as high household debt, strict credit conditions, and rising costs causing market contraction,
decreasing project launches by 43.72\% in 2024\cite{nationthailand1}. However, it also presents opportunities for recovery through foreign investment driven by tourism recovery,
government incentives, and technological advancements. Condominiums remain attractive for rental yields and foreign buyers, while the housing market has moderate growth supported by infrastructure development.

% Additionally, Thailand's real estate information ecosystem suffers from several structural limitations,
% including outdated listings, lack of official transaction data, and data
% fragmentation\cite{bangkokpost1}. Addressing these issues is important for improving Thailand's
% market accessibility and decision-making for both homebuyers and investors.

Crucially, these market opportunities can significantly undermined by Thailand's real estate information ecosystem, which suffers from several structural limitations, including outdated listings,
lack of official transaction data, and data fragmentation\cite{bangkokpost1}. Addressing these issues is essential for maximizing Thailand's market potential and enabling effective decision-making
for both homebuyers and investors seeking to capitalize on the available opportunities.

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\section{Problem Statement}
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Even with large amounts of real estate data available through various channels including
property listings, historical transaction records, and news, investors still
face challenges in aggregating, contextualizing, and deriving action from these
sources. Additionally, the Thai real estate market has different characteristics
from other countries because of the limited availability of official property
transaction records, listing duplication across platforms, and less standardized
data compared to more mature markets.

Current platforms in Thailand like DDProperty, Hipflat, and Baania lack many
important contextual factors such as climate risk assessments, neighborhood-specific
news, and local beliefs that influence property valuation and real estate
investment in the long term.

While platforms like Zillow and House Canary have advanced and comprehensive
real estate analytics, predictive analytics, and user-friendly interfaces, they do
not operate in Thailand. Even if these advanced platforms were to enter the Thai
market, they would face challenges adapting their algorithms to the local context.
Their models are calibrated to markets with standardized property
classifications, valuations that vary by developers, and consistent transaction
data—elements that are limited in Thailand's real estate ecosystem.

The BorBann platform addresses these challenges and aims to help users by
creating a real estate data platform integrated with artificial intelligence, geospatial
analytics, and data aggregation by focusing on analytics rather than transaction
facilitation.

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\section{Solution Overview}
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BorBann will function as real estate data platform to users, integrating multiple
data sources with advanced analytics. Below are the features and their details.

\subsection{Customizable Automated Data Integration Pipeline}  
\begin{itemize}  
    \item \textbf{Automated schema inference:} Analyze website structures to identify and extract key data elements  
    \item \textbf{Field mapping:} Recognize equivalent fields across different sources (e.g., "price" vs "cost")  
    \item \textbf{Integration framework:} Seamless connection with data export systems  
    \item \textbf{Multi-source support:} Process data from websites, APIs, and uploaded files  
\end{itemize}  

\subsection{Retrain Model with Data from Pipeline}  
\begin{itemize}  
    \item \textbf{Custom prediction model:} Create custom prediction models by combining their pipeline data with platform data sources
\end{itemize}

\subsection{Local Contextual Analytics}  
\begin{itemize}  
    \item \textbf{Environmental risk assessment:} Evaluate flood risk, natural disaster vulnerability, and air quality  
    \item \textbf{Facility proximity analysis:} Calculate accessibility to schools, hospitals, transit, and commercial centers  
    \item \textbf{Neighborhood quality scoring:} Generate composite metrics for area evaluation  
\end{itemize}  
  
\subsection{Explainable Price Prediction Model}  
\begin{itemize}  
    \item \textbf{Feature importance analysis:} Quantify and rank factors influencing property prices  
    \item \textbf{Adjustable characteristics modeling:} Adjust property characteristics to visualize price impacts  
    \item \textbf{Confidence intervals:} Provide lower/upper price bounds for realistic expectations  
    \item \textbf{Factor categorization:} Group influences by type (property features, location, market trends)  
    \item \textbf{Natural language explanations:} Generate readable summaries of price determinants  
    \item \textbf{Visual breakdowns:} Display contribution percentages and relationship graphs  
\end{itemize}  
  
\subsection{Geospatial Visualization}  
\begin{itemize}  
    \item \textbf{Heatmap generation:} Create density visualizations for environmental factors, pricing, and metrics  
    \item \textbf{Geospatial analytics:} Calculate analytics for custom geographic areas  
\end{itemize}
\newpage

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\section{Target User}
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\begin{table}[htbp]
	\centering
	\renewcommand{\arraystretch}{1.3}
	\begin{tabular}{>{\raggedright\arraybackslash}p{0.25\textwidth}>{\raggedright\arraybackslash}p{0.35\textwidth}>{\raggedright\arraybackslash}p{0.3\textwidth}}
		\toprule \rowcolor[gray]{0.9} \textbf{User Type}        & \textbf{Description}                                                                                 & \textbf{Needs}                                                                                                 \\
		\midrule \textbf{Real Estate Investors}                 & Individuals focused on maximizing long-term investment, including foreign investors & \begin{itemize}\item Investment analysis

\item Supporting data for decision making
\end{itemize}            \\
		\addlinespace \rowcolor[gray]{0.95} \textbf{Homebuyers} & First-time purchasers, residents looking to relocate within Thailand, and expats seeking housing     & \begin{itemize}\item Property comparisons

\item Neighborhood insights

\item Pricing guidance\end{itemize}    \\
		\bottomrule
	\end{tabular}
	\caption{Target Users and Their Needs}
	\label{tab:users}
\end{table}

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\section{Benefit}
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The BorBann platform will provide numerous benefits to the Thai real estate market.
It improves market transparency by enhancing accessibility to market information.
It also helps both homebuyers and investors reduce research time, achieve lower
transaction risks, and discover overlooked investment opportunities.
Additionally, the platform will effectively represent the unique characteristics
of the Thai real estate market.

\newpage

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\section{Terminology}
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\begin{table}[htbp]
	\centering
	\renewcommand{\arraystretch}{1.3}
	\begin{tabular}{>{\raggedright\arraybackslash}p{0.3\textwidth}>{\raggedright\arraybackslash}p{0.6\textwidth}}
		\toprule \rowcolor[gray]{0.9} \textbf{Term}                           & \textbf{Definition}                                                                                                                                                       \\
		\addlinespace \textbf{Local Analytics}                           & Analysis focused on extremely specific geographic areas, such as neighborhoods or even individual streets, to provide highly relevant insights.                           	  \\
		\addlinespace \rowcolor[gray]{0.95} \textbf{Price Prediction Model}   & An algorithm or statistical model that forecasts property values based on historical data, market trends, and various property characteristics.                           \\
		\addlinespace \textbf{Proximity Analysis}                             & The study of spatial relationships between geographic features, typically to evaluate the distance between properties and amenities or services.                          \\
		\addlinespace \rowcolor[gray]{0.95} \textbf{Geospatial Visualization} & The graphical representation of data with a geographic or spatial component, often through maps and interactive displays.                                                 \\
		\bottomrule
	\end{tabular}
	\caption{Terminology}
	\label{tab:terminology}
\end{table}


