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Real estate transactions have traditionally been slow, paperwork-heavy, and opaque. Apps like Property Finder have transformed the industry by making property discovery, listing management, search, and communication fast, intuitive, and accessible on mobile and web. Today’s users expect high-quality images, accurate listings, interactive maps, intelligent search filters, alerts, and seamless agent interactions right in their palms.
Property Finder and similar platforms have become indispensable tools for buyers, renters, investors, and real estate professionals across markets. Their success highlights a broader opportunity in property tech (PropTech): well-designed, data-rich property search apps solve real problems and generate high user engagement.
Understanding the cost to build a real estate app like Property Finder requires analyzing key components: user experience, search sophistication, backend systems, integration with third-party services, and long-term maintenance.
This is Part 1 of a four-part series. Part 1 focuses on the core concept of real estate apps, why they matter, the business model, and foundational decisions that drive cost.
At its core, a real estate platform connects property seekers with listings and agents. It aggregates property data, displays it with search and filter capabilities, and provides communication tools for inquiries and engagement. Users expect a polished interface, real-time updates, and trustworthy information.
A Property Finder–like app typically supports two sides of the market: consumers browsing and agents/listing owners uploading and managing property listings. Supporting this two-sided ecosystem adds complexity and cost.
The global real estate market is massive, and digital adoption continues to grow. Buyers and renters increasingly rely on mobile apps for browsing and decision-making. Real estate professionals use these platforms to reach wider audiences and manage inquiries efficiently.
Understanding your target geography matters. Markets with high digital adoption demand advanced features such as virtual tours and map layers, whereas emerging markets may prioritize basic search and localized listings.
Property Finder succeeds because it simplifies search, displays rich media, and connects users with agents efficiently. A competitive real estate app must go beyond basic listing search: it should deliver accurate, up-to-date property data, fast map interactions, and useful insights such as price trends and neighborhood amenities.
Differentiation also affects cost. A basic MVP focusing on core search and listing display is cheaper than a full-featured platform with AI recommendations, CRM integration for agents, and advanced analytics.
Typical property app use cases include:
Advanced use cases such as virtual tours, mortgage calculators, AI-recommendation feeds, and analytics increase development complexity and cost.
Deciding which use cases to include in the first version is one of the most important cost-control decisions.
Users expect smooth search, intuitive navigation, clear property details, and high-quality images. Performance matters: slow filters or laggy maps drive users away.
Design complexity directly affects cost. Polished UI/UX with custom animations, responsive layouts, and accessibility features require higher design and QA budgets.
Determining whether your app will support renters, buyers, and/or sellers influences cost. Supporting commercial real estate, global listings, or multi-language capabilities also adds complexity.
Agent interfaces, property management dashboards, and administrative control panels further extend development scope.
Real estate apps thrive on data. Most mature platforms integrate with MLS (Multiple Listing Services) or property data feeds to import listings automatically.
Each data source may require custom integration, normalization logic, and ongoing maintenance. Data agreements, licensing fees, and API customization add cost.
Platforms without data integration require manual listing uploads, which limits scalability.
Interactive maps are central to property search. Users expect accurate location pins, clustering, neighborhoods, proximity to amenities, and route directions.
Map services (e.g., Google Maps or alternatives) have usage costs based on requests. Implementing map features increases both development and ongoing expenses.
Users expect timely alerts for new listings, price drops, and saved search matches.
Implementing reliable push notifications, email alerts, and SMS alerts increases complexity, especially when personalized based on user preferences.
Notification infrastructure adds both development cost and ongoing messaging service fees.
A real estate app is only as strong as its content and management tools. Admin dashboards allow internal teams to monitor listings, user behavior, analytics, and compliance.
Agent portals allow listing uploads, edits, photo management, and inquiry tracking.
These dashboards require secure authentication, role-based access, and data validation workflows. Building them adds to development time and testing.
Many cost overruns happen because core architectural and product decisions are rushed. Choosing the wrong data sources, ignoring scalability, or underestimating search and map complexity leads to expensive rework.
Early investment in planning, UX design, and API strategy reduces long-term cost and risk.
This is where experienced development partners add value by aligning technical decisions with product and business goals.
The cost of developing a real estate app like Property Finder is directly influenced by the number, depth, and quality of functional modules included in the platform. Real estate apps are not simple listing catalogs. They are data-heavy, search-intensive, and two-sided platforms that must serve both property seekers and property listers efficiently.
Each module adds development time, testing effort, infrastructure load, and long-term maintenance overhead. Understanding these modules individually helps estimate cost realistically and avoid under-budgeting.
Most real estate apps allow users to browse listings without registration, but advanced features such as saving properties, alerts, and contacting agents require accounts.
User profile systems include authentication, profile management, preferences, saved searches, and favorites. Supporting social logins, email login, and phone-based authentication increases development scope but improves conversion.
While this module is not the most expensive, it becomes more complex when combined with personalization and cross-device synchronization.
Search is the heart of a real estate app and one of the biggest cost drivers.
Users expect fast, accurate search with multiple filters such as location, price range, property type, size, number of bedrooms, furnishing status, and amenities. Advanced platforms also include filters for developer, completion status, rental yield, or pet-friendliness.
Implementing high-performance search requires optimized databases, indexing strategies, and sometimes dedicated search engines. The more filters and combinations supported, the higher the backend complexity and infrastructure cost.
Map view is a core feature in apps like Property Finder. Users want to explore properties visually using location pins, clusters, and neighborhood boundaries.
Map integration involves geolocation services, map SDKs, clustering logic, and real-time updates as users zoom or pan. Advanced features include drawing custom search areas, proximity search to schools or metro stations, and neighborhood insights.
Map APIs often have usage-based pricing, which adds ongoing operational cost beyond development.
Property detail pages must present large volumes of information clearly. These pages typically include property descriptions, images, videos, floor plans, location maps, agent details, and call-to-action buttons.
Media management is a hidden cost driver. High-resolution photos, videos, and virtual tours require storage, compression, and fast delivery through CDNs. As listing volume grows, storage and bandwidth costs increase.
Supporting rich media improves engagement but increases both development and infrastructure expenses.
To scale listings, real estate apps must provide tools for agents and property owners to upload and manage listings.
This includes property creation forms, image uploads, pricing updates, availability status, and inquiry tracking. Validation workflows ensure listing quality and reduce spam.
Agent dashboards often include lead management, analytics, and performance insights. These features significantly increase backend and frontend development scope.
Communication between users and agents is central to real estate platforms. Basic contact options include phone calls and email inquiries.
Advanced platforms offer in-app messaging, chat history, and lead routing. Implementing messaging requires real-time systems, notification triggers, and moderation tools.
While messaging improves conversion, it adds complexity to system architecture and increases maintenance effort.
Property seekers rely heavily on alerts. They expect notifications when new listings match their criteria or when prices change.
Implementing saved searches requires background jobs that continuously match new listings with user preferences. Notification systems must handle personalization at scale.
Push notifications, emails, and sometimes SMS alerts add recurring service costs in addition to development effort.
Advanced real estate apps use personalization to surface relevant listings based on browsing behavior, preferences, and engagement patterns.
Basic recommendation logic is rule-based, while advanced systems use machine learning. AI-driven recommendations increase development and data infrastructure costs but significantly improve engagement and retention.
Personalization becomes more valuable as user base and listing volume grow.
A robust admin panel is essential for platform operations. Admins need to manage listings, users, agents, reports, and analytics.
Moderation tools help detect fake listings, duplicate content, and policy violations. Manual review workflows add operational cost but improve platform trust.
Building secure admin dashboards with role-based access increases development effort but is mandatory for long-term scalability.
Analytics help platform owners understand user behavior, popular locations, conversion funnels, and agent performance.
Implementing analytics dashboards requires event tracking, data storage, and visualization tools. While not user-facing, analytics are critical for product optimization and monetization.
Many Property Finder–like apps monetize through featured listings, subscription plans for agents, or lead-based pricing.
Implementing monetization requires payment gateways, invoicing, access control, and reporting systems. These features add both development and compliance considerations.
Early monetization planning avoids costly rework later.
The biggest cost lever is deciding what to include in the first release. Attempting to replicate every feature of Property Finder at launch significantly increases budget and time to market.
Successful platforms launch with strong core search, listing quality, and basic agent tools. Advanced features such as AI recommendations, virtual tours, and analytics are added incrementally.
A phased roadmap reduces risk and aligns investment with user adoption.
The technical architecture of a real estate app like Property Finder is one of the most important factors influencing both initial development cost and long-term operating expenses. While users interact mainly with search results and property pages, the backend must process large datasets, complex queries, media files, and real-time interactions reliably and at scale.
A poorly designed architecture may reduce early development cost but quickly becomes expensive due to performance issues, frequent downtime, and the need for major refactoring. A scalable architecture requires higher upfront investment but significantly lowers total cost of ownership over time.
The backend stack must support high-volume read operations, complex search queries, and concurrent users. Common backend choices include Node.js, Java (Spring Boot), and Python-based frameworks.
Node.js is often preferred for real-time features such as messaging and notifications. Java-based systems are widely used for enterprise-grade stability and scalability. Python works well for analytics, AI-based recommendations, and data processing layers.
Many mature PropTech platforms use a hybrid approach, separating search services, listing services, and user services into independent components. This modularity increases development cost initially but improves performance and scalability.
Search is the most performance-sensitive component of a real estate app. Users expect instant results even with multiple filters applied.
To achieve this, platforms often use dedicated search engines rather than relying solely on traditional databases. These engines index property attributes such as price, location, size, amenities, and availability.
Implementing advanced search infrastructure increases development complexity and infrastructure cost but is essential for fast response times and user satisfaction. As listing volume grows, search optimization becomes a continuous cost factor.
Real estate platforms manage multiple data types: structured data (property attributes, users, agents), semi-structured data (descriptions, tags), and unstructured data (images, videos).
Relational databases are commonly used for transactional data, while NoSQL databases or object storage systems handle media and logs. Proper data modeling ensures accurate filtering, faster queries, and easier feature expansion.
Data normalization and deduplication are especially important when integrating multiple listing sources. Implementing these processes adds development effort but improves data quality and platform trust.
High-quality images and videos are critical for property discovery but significantly increase infrastructure costs.
Media files must be stored securely, optimized for different screen sizes, and delivered quickly. Content Delivery Networks reduce latency and bandwidth costs but add recurring expenses.
Video features such as virtual tours and walkthroughs further increase storage, processing, and streaming costs. Deciding whether to include these features in early versions has a major impact on budget.
Real estate apps rely heavily on third-party services. Common integrations include map services, geocoding APIs, payment gateways, analytics tools, notification services, and property data feeds.
Each integration requires development, testing, monitoring, and ongoing maintenance. API usage fees contribute to operational costs.
Integration with MLS systems or proprietary listing feeds is often complex due to data standards, update frequencies, and licensing requirements.
While real estate apps are not as regulated as FinTech platforms, they still handle sensitive user data such as contact details, messages, and transaction-related information.
Implementing secure authentication, authorization, data encryption, and access controls adds to development cost. Protecting agent dashboards and admin panels is especially important.
Security investment reduces risk of data breaches and improves platform credibility, which directly affects user adoption.
Traffic on property platforms can spike due to new listings, promotions, or market events. Systems must scale automatically to handle peak loads.
Load balancing, caching, and database replication improve reliability but increase infrastructure cost. Performance testing and monitoring are ongoing requirements.
Scalability planning is essential for platforms targeting multiple cities or countries.
Behind the scenes, admins require tools to manage listings, monitor activity, moderate content, and generate reports.
Admin systems often include role-based access, audit logs, and data export tools. These features are invisible to end users but critical for platform operations.
Building robust admin infrastructure adds development cost but reduces operational friction and support overhead.
Continuous integration and deployment pipelines ensure stable releases and faster updates. Setting up automated testing, staging environments, and rollback mechanisms increases initial cost.
Ongoing maintenance includes server updates, bug fixes, performance tuning, and dependency upgrades. These recurring costs must be planned from the beginning.
Advanced real estate apps increasingly use AI for price estimation, demand forecasting, and personalized recommendations.
Implementing AI features requires data pipelines, analytics infrastructure, and model training. While optional in early stages, these features significantly increase cost and differentiation.
Many PropTech startups underestimate technical complexity and end up rebuilding core systems as usage grows. This dramatically increases total cost.
Experienced development partners help choose the right architecture, data strategy, and infrastructure from the start, preventing expensive redesigns.
Abbacus Technologies brings experience in building scalable real estate and marketplace platforms, aligning technical decisions with long-term business goals and cost efficiency.
Estimating the cost of developing a real estate app like Property Finder requires looking at the product as a long-term digital platform rather than a one-time app build. The final budget depends on feature depth, geography, data integrations, scalability requirements, and monetization strategy.
A basic MVP version that includes property browsing, search with essential filters, property detail pages, map view, user accounts, and basic agent contact features can be built with a controlled budget. This version is suitable for validating market demand in a specific city or region.
A mid-level platform adds advanced filters, saved searches, alerts, agent dashboards, listing management, analytics, and better media handling. This level is where many platforms start generating revenue through agent subscriptions or featured listings.
A full-scale Property Finder–like platform includes MLS integrations, AI-based recommendations, price insights, virtual tours, multilingual support, advanced CRM tools for agents, and enterprise-grade admin systems. This version requires a significantly higher investment but is capable of operating at regional or international scale.
In addition to development, recurring costs such as cloud hosting, map API usage, media storage, notification services, analytics tools, and support operations must be factored into total cost.
Real estate platforms are best built in phases to manage cost and risk effectively.
The planning and discovery phase includes market research, feature definition, UX design, data strategy, and technical architecture. Although this phase represents a smaller portion of the budget, it has a major impact on long-term cost efficiency.
The core development phase involves backend services, mobile and web apps, search infrastructure, integrations, and admin dashboards. This phase consumes the largest share of the budget and timeline.
The testing and launch phase focuses on performance optimization, security checks, data accuracy validation, and user acceptance testing. Given the data-heavy nature of real estate apps, testing is critical.
Post-launch, the platform enters continuous improvement mode, where features are refined, new cities are added, and advanced capabilities are rolled out.
Many real estate startups underestimate post-launch costs. In practice, operational expenses grow steadily as the platform scales.
Infrastructure costs increase with traffic, listing volume, and media usage. Map services and geolocation APIs incur usage-based fees. Storage and CDN costs grow as high-quality images and videos accumulate.
Maintenance includes bug fixes, OS updates, dependency upgrades, security patches, and performance tuning. As listings and users grow, moderation and support costs also increase.
If the platform operates across regions, localization, data updates, and regional compliance add further expense.
Property Finder–like platforms use multiple monetization strategies rather than relying on one revenue source.
Agent subscriptions are one of the most common models. Agents pay monthly or annually for listing quotas, leads, or premium dashboards.
Featured or promoted listings allow agents or developers to pay for higher visibility. This model works well in competitive markets.
Lead-based pricing charges agents for verified inquiries or contacts generated through the platform.
Advertising and partnerships generate additional revenue from developers, mortgage providers, or moving services.
Some advanced platforms also monetize through market insights, data analytics, or enterprise tools.
Choosing the right monetization mix depends on market maturity and user behavior.
Growth in real estate apps is driven by trust, data quality, and usability rather than virality.
Early growth typically focuses on a limited geography to ensure listing accuracy and liquidity. Expanding into new cities or countries increases cost due to data sourcing, localization, and marketing.
Partnerships with developers, brokers, and property management firms help accelerate adoption while controlling acquisition costs.
Marketing spend often includes content marketing, SEO, paid search, and offline agent outreach. Education and onboarding are critical for agent adoption.
Real estate platforms are highly competitive. Poor search performance, outdated listings, or inaccurate data quickly erode user trust.
Underinvesting in architecture or data quality often leads to expensive fixes later. Likewise, launching with too many features without validating demand can inflate costs without improving retention.
The biggest long-term risk is building a platform that cannot scale economically or differentiate meaningfully.
Building a Property Finder–like platform requires expertise in search systems, map-based UX, scalable backend architecture, and marketplace dynamics.
An experienced partner helps define the right MVP, choose scalable technologies, and design systems that grow efficiently. This reduces total cost of ownership rather than just upfront spend.
This is where > adds value. With experience in PropTech and marketplace platforms, Abbacus Technologies helps businesses build real estate apps that balance performance, scalability, and monetization from day one.
Developing a real estate app like Property Finder is a strategic investment in a data-driven, two-sided marketplace. The total cost is influenced by feature scope, search complexity, data integrations, media handling, scalability planning, and long-term operational needs.
Developing a real estate app like Property Finder is not a simple app development task. It is the creation of a data-intensive, search-driven, two-sided digital marketplace that must balance user experience, data accuracy, scalability, and monetization. The total cost is shaped far more by long-term architectural and product decisions than by surface-level design or screen count.
At its core, a Property Finder–style platform connects property seekers (buyers and renters) with property providers (agents, brokers, developers, and owners). To succeed, the app must deliver fast and accurate property search, rich media presentation, map-based discovery, and seamless communication. Trust is the foundation of adoption. Users will only return if listings are accurate, updated, and easy to explore.
The biggest cost driver is feature scope. A basic MVP with property browsing, essential filters, map view, property detail pages, and basic agent contact can be built with a controlled budget and is ideal for validating demand in a single city or region. However, as soon as the platform expands to include advanced filters, saved searches, alerts, agent dashboards, analytics, and paid listings, development cost rises significantly.
Search and filtering systems are especially expensive because they must remain fast even with thousands or millions of listings. Advanced filtering, sorting, and ranking require optimized databases and dedicated search infrastructure. Poor search performance directly leads to user churn, making this an area where cost cutting is risky.
Map-based discovery is another major cost factor. Users expect interactive maps with clustering, neighborhoods, proximity search, and smooth zooming. Map APIs typically charge based on usage, so both development and ongoing operational costs must be planned carefully.
Media handling adds hidden expenses. High-resolution images, videos, and virtual tours improve engagement but increase storage, processing, and bandwidth costs. As listing volume grows, these infrastructure costs grow continuously.
On the supply side, agent and owner dashboards are essential for scale. These tools allow listing uploads, edits, media management, pricing updates, and inquiry tracking. Advanced platforms also offer analytics and lead management. While these features are not visible to end users, they significantly increase backend and frontend development effort.
Notifications and alerts drive retention. Saved searches, price drop alerts, and new listing notifications require background processing and personalized matching logic. Messaging systems, whether in-app or via contact routing, further add to system complexity.
From a technical standpoint, architecture decisions have a long-term impact on cost. A scalable, modular backend costs more upfront but prevents expensive rewrites later. Real estate platforms handle heavy read traffic, complex queries, and large media files, making performance optimization and scalability essential.
Third-party integrations also affect cost. Map services, analytics tools, notification services, payment gateways, and property data feeds all require development, testing, and ongoing maintenance. MLS or listing feed integrations often involve licensing fees and data normalization work.
Beyond development, operational costs are ongoing and often underestimated. Cloud hosting, search infrastructure, map API usage, media storage, CDN delivery, notifications, support, and moderation all add recurring expenses. As the platform expands to new cities or countries, localization, data sourcing, and marketing further increase cost.
Monetization plays a key role in sustainability. Most Property Finder–like apps rely on agent subscriptions, featured listings, lead-based pricing, and advertising or partnerships. Monetization features require payment systems, access control, and reporting tools, which must be planned early to avoid rework.
Growth in real estate platforms is driven by data quality and trust, not virality. Launching city by city is often more cost-effective than broad expansion. Strong onboarding and education for agents are as important as user acquisition on the consumer side.
The greatest long-term risk is poor execution. Slow search, outdated listings, weak architecture, or unreliable data lead to user drop-off and expensive fixes later. Many platforms fail not because of lack of demand, but because early shortcuts create scalability and performance bottlenecks.
This is why choosing the right development approach and partner matters. Experienced teams help define a realistic MVP, select scalable technologies, and align features with monetization and growth strategy. Abbacus Technologies brings this execution-focused mindset by building PropTech platforms that balance performance, scalability, and cost efficiency from the start.
In conclusion, the cost to develop a real estate app like Property Finder is the result of feature ambition, search complexity, data integration depth, architectural quality, and long-term operational planning. A phased roadmap, strong technical foundation, and focus on trust and usability turn development cost into a sustainable, revenue-generating digital real estate marketplace rather than an expensive experiment.
Building a real estate app like Property Finder is a long-term platform investment, not a one-time software project. The real cost is not defined by how many screens the app has, but by how effectively it handles data accuracy, fast search, trust, scalability, and monetization in a highly competitive market. Successful real estate platforms sit at the intersection of technology, data, and marketplace dynamics, and each of these layers contributes significantly to total cost.
At the foundation, a Property Finder–style app is a two-sided marketplace. On one side are buyers and renters who expect instant access to accurate, up-to-date property information. On the other side are agents, brokers, developers, and owners who need reliable tools to publish listings, manage leads, and measure performance. Supporting both sides simultaneously adds complexity that directly impacts development effort, infrastructure needs, and maintenance cost.
One of the biggest contributors to cost is search and discovery. Real estate search is far more complex than standard eCommerce search. Users apply multiple filters at once, expect near-instant results, and frequently switch between list and map views. As listings scale into the tens or hundreds of thousands, performance becomes a critical challenge. Dedicated search infrastructure, proper indexing, caching, and optimization are required. Cutting corners here often leads to slow results, frustrated users, and costly re-engineering later.
Closely tied to search is map-based exploration, which has become a default expectation. Users want to visually explore neighborhoods, compare areas, and understand proximity to schools, transport, or workplaces. Implementing smooth map interactions, clustering, and geo-search adds both development cost and ongoing API usage costs. As traffic grows, map services become a recurring expense that must be planned into the business model.
Another major cost factor is data quality and integration. High-performing real estate apps depend on reliable property data. Integrating MLS systems, broker feeds, or third-party property databases requires custom normalization logic, validation workflows, and constant monitoring. Poor data quality damages trust instantly. Maintaining clean, accurate listings is an ongoing operational cost, not just a development task.
Media handling is a hidden but significant expense. High-resolution photos, videos, floor plans, and virtual tours dramatically improve engagement and conversion, but they increase storage, processing, and delivery costs. As the platform grows, media storage and CDN usage become major recurring expenses. Decisions about video, 3D tours, and image quality directly affect both UX and infrastructure budgets.
On the supply side, agent and owner tools are essential for scalability and monetization. Dashboards for uploading listings, managing images, tracking inquiries, and analyzing performance require complex backend logic and secure access control. Advanced platforms also provide CRM-like features, analytics, and lead scoring. While these tools are not visible to consumers, they account for a substantial portion of development cost.
Notifications, alerts, and messaging are key to retention and conversion. Saved searches, price drop alerts, and instant inquiry notifications require background processing, personalized matching, and reliable delivery systems. Messaging features add real-time infrastructure and moderation considerations. These systems increase both development and operational complexity but are essential for engagement.
From a technical perspective, architecture decisions define long-term cost. A scalable, modular architecture costs more upfront but prevents expensive rewrites as traffic and data grow. Real estate apps experience heavy read traffic, sudden spikes, and data-heavy workloads. Load balancing, caching, replication, and monitoring are not optional if the platform is expected to grow beyond a single city.
Beyond development, ongoing operational costs often exceed initial build costs over time. These include cloud hosting, search infrastructure, map API usage, media storage, CDN delivery, notification services, analytics tools, customer support, and content moderation. As the platform expands geographically, localization, new data sources, and marketing spend add further cost layers.
Monetization strategy strongly influences development scope. Most Property Finder–like platforms rely on agent subscriptions, featured listings, lead-based pricing, and advertising partnerships. Implementing monetization requires payment gateways, invoicing, access controls, reporting, and fraud prevention. Planning monetization early avoids expensive refactoring later.
Growth in real estate platforms is trust-driven, not viral. Users return only if listings are accurate and relevant. Agents stay only if leads convert. This means growth is often city-by-city, with significant upfront effort to seed listings and onboard agents. Marketing costs are closely tied to education and relationship building, not just digital ads.
The biggest long-term risk is poor execution. Platforms that rush to launch with weak search, outdated listings, or fragile architecture often face high churn and expensive rebuilds. Many real estate apps fail not because of lack of demand, but because early shortcuts compound into technical and operational debt.
This is why execution experience matters more than initial budget. The real savings come from doing the right things early, not from building cheaply. Experienced partners help define a realistic MVP, choose scalable technologies, and align features with monetization and growth strategy.
In this context, Abbacus Technologies plays a critical role. With experience in marketplace and PropTech development, Abbacus Technologies focuses on building real estate platforms that balance performance, scalability, and cost efficiency. Their approach reduces total cost of ownership by preventing architectural mistakes and aligning development with long-term business goals.
In conclusion, the cost to develop a real estate app like Property Finder is shaped by search complexity, data integration depth, media handling, architectural quality, monetization readiness, and operational planning. A phased roadmap, strong technical foundation, and focus on trust transform development cost into a sustainable, revenue-generating digital real estate ecosystem rather than a short-lived product.
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