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160 lines
10 KiB
TeX
160 lines
10 KiB
TeX
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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.
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Real estate market remains a key sector for people like homebuyers and investors,
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influenced by complex characteristics with heterogeneous nature and affected by numerous elements like policies and consumer trends.
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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}.
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Thailand's real estate market faces both challenges and opportunities in the current economic climate. The real estate market in Thailand,
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especially in Bangkok in 2024-2025, faces challenges such as high household debt, strict credit conditions, and rising costs causing market contraction,
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decreasing project launches by 43.72\% in 2024\cite{nationthailand1}. However, it also presents opportunities for recovery through foreign investment driven by tourism recovery,
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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.
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% Additionally, Thailand's real estate information ecosystem suffers from several structural limitations,
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% including outdated listings, lack of official transaction data, and data
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% fragmentation\cite{bangkokpost1}. Addressing these issues is important for improving Thailand's
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% market accessibility and decision-making for both homebuyers and investors.
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Crucially, these market opportunities can significantly undermined by Thailand's real estate information ecosystem, which suffers from several structural limitations, including outdated listings,
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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
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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
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property listings, historical transaction records, and news, investors still
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face challenges in aggregating, contextualizing, and deriving action from these
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sources. Additionally, the Thai real estate market has different characteristics
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from other countries because of the limited availability of official property
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transaction records, listing duplication across platforms, and less standardized
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data compared to more mature markets.
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Current platforms in Thailand like DDProperty, Hipflat, and Baania lack many
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important contextual factors such as climate risk assessments, neighborhood-specific
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news, and local beliefs that influence property valuation and real estate
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investment in the long term.
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While platforms like Zillow and House Canary have advanced and comprehensive
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real estate analytics, predictive analytics, and user-friendly interfaces, they do
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not operate in Thailand. Even if these advanced platforms were to enter the Thai
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market, they would face challenges adapting their algorithms to the local context.
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Their models are calibrated to markets with standardized property
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classifications, valuations that vary by developers, and consistent transaction
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data—elements that are limited in Thailand's real estate ecosystem.
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The BorBann platform addresses these challenges and aims to help users by
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creating a real estate data platform integrated with artificial intelligence, geospatial
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analytics, and data aggregation by focusing on analytics rather than transaction
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facilitation.
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\section{Solution Overview}
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BorBann will function as real estate data platform to users, integrating multiple
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data sources with advanced analytics. Below are the features and their details.
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\subsection{Customizable Automated Data Integration Pipeline}
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\begin{itemize}
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\item \textbf{Automated schema inference:} Analyze website structures to identify and extract key data elements
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\item \textbf{Field mapping:} Recognize equivalent fields across different sources (e.g., "price" vs "cost")
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\item \textbf{Integration framework:} Seamless connection with data export systems
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\item \textbf{Multi-source support:} Process data from websites, APIs, and uploaded files
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\end{itemize}
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\subsection{Retrain Model with Data from Pipeline}
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\begin{itemize}
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\item \textbf{Custom prediction model:} Create custom prediction models by combining their pipeline data with platform data sources
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\end{itemize}
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\subsection{Local Contextual Analytics}
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\begin{itemize}
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\item \textbf{Environmental risk assessment:} Evaluate flood risk, natural disaster vulnerability, and air quality
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\item \textbf{Facility proximity analysis:} Calculate accessibility to schools, hospitals, transit, and commercial centers
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\item \textbf{Neighborhood quality scoring:} Generate composite metrics for area evaluation
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\end{itemize}
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\subsection{Explainable Price Prediction Model}
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\begin{itemize}
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\item \textbf{Feature importance analysis:} Quantify and rank factors influencing property prices
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\item \textbf{Adjustable characteristics modeling:} Adjust property characteristics to visualize price impacts
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\item \textbf{Confidence intervals:} Provide lower/upper price bounds for realistic expectations
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\item \textbf{Factor categorization:} Group influences by type (property features, location, market trends)
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\item \textbf{Natural language explanations:} Generate readable summaries of price determinants
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\item \textbf{Visual breakdowns:} Display contribution percentages and relationship graphs
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\end{itemize}
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\subsection{Geospatial Visualization}
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\begin{itemize}
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\item \textbf{Heatmap generation:} Create density visualizations for environmental factors, pricing, and metrics
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\item \textbf{Geospatial analytics:} Calculate analytics for custom geographic areas
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\end{itemize}
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\newpage
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\section{Target User}
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\begin{table}[htbp]
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\centering
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\renewcommand{\arraystretch}{1.3}
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\begin{tabular}{>{\raggedright\arraybackslash}p{0.25\textwidth}>{\raggedright\arraybackslash}p{0.35\textwidth}>{\raggedright\arraybackslash}p{0.3\textwidth}}
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\toprule \rowcolor[gray]{0.9} \textbf{User Type} & \textbf{Description} & \textbf{Needs} \\
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\midrule \textbf{Real Estate Investors} & Individuals focused on maximizing long-term investment, including foreign investors & \begin{itemize}\item Investment analysis
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\item Supporting data for decision making
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\end{itemize} \\
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\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
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\item Neighborhood insights
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\item Pricing guidance\end{itemize} \\
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\bottomrule
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\end{tabular}
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\caption{Target Users and Their Needs}
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\label{tab:users}
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\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.
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It improves market transparency by enhancing accessibility to market information.
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It also helps both homebuyers and investors reduce research time, achieve lower
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transaction risks, and discover overlooked investment opportunities.
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Additionally, the platform will effectively represent the unique characteristics
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of the Thai real estate market.
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\newpage
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\section{Terminology}
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\begin{table}[htbp]
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\centering
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\renewcommand{\arraystretch}{1.3}
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\begin{tabular}{>{\raggedright\arraybackslash}p{0.3\textwidth}>{\raggedright\arraybackslash}p{0.6\textwidth}}
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\toprule \rowcolor[gray]{0.9} \textbf{Term} & \textbf{Definition} \\
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\addlinespace \textbf{Local Analytics} & Analysis focused on extremely specific geographic areas, such as neighborhoods or even individual streets, to provide highly relevant insights. \\
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\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. \\
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\addlinespace \textbf{Proximity Analysis} & The study of spatial relationships between geographic features, typically to evaluate the distance between properties and amenities or services. \\
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\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. \\
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\bottomrule
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\end{tabular}
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\caption{Terminology}
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\label{tab:terminology}
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\end{table}
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