AI Assistant for Real Estate Search in Mobile Apps

AI Assistant for Real Estate Search in Mobile Apps

A potential buyer states intentions, not filters: "I want a bright apartment in a quiet area, near the metro, under 10 million rubles." Traditional search forms with fields like "price from/to", "number of rooms", "district" miss nuances — defining a "quiet area" or "walking distance to metro" requires context. We develop an AI assistant that translates free text into structured search parameters and explains to the user why each property matches their request. Get in touch to discuss your data and requirements — we'll adapt the solution to your listing database. Over five years in mobile development, we have delivered more than 10 projects in this domain.

How the AI Assistant Understands User Queries

The user describes their wish in free text. Through function calling, structured criteria are extracted. This is a mechanism where the language model calls a predefined function with parameters extracted from the message. For example, from the phrase "looking for a studio up to 5 million, with balcony, on Akademicheskaya", the model populates a JSON:

let extractFiltersFunction: [String: Any] = [ "name": "extract_search_filters", "description": "Extract real estate search criteria from user message", "parameters": [ "type": "object", "properties": [ "property_type": ["type": "string", "enum": ["apartment", "house", "studio", "commercial"]], "min_rooms": ["type": "integer"], "max_rooms": ["type": "integer"], "min_area_sqm": ["type": "number"], "max_price": ["type": "number"], "metro_walk_minutes": ["type": "integer", "description": "Max walking time to metro"], "districts": ["type": "array", "items": ["type": "string"]], "floor_preference": ["type": "string", "enum": ["not_ground", "not_top", "high", "any"]], "must_haves": ["type": "array", "items": ["type": "string"], "description": "Required features: parking, balcony, new_building, quiet_street, etc."], "deal_type": ["type": "string", "enum": ["buy", "rent"]] ] ] ] 

This schema is based on OpenAI Function Calling Guide. The process consists of three steps: 1. User inputs text. 2. Model extracts parameters via function calling. 3. If necessary, asks clarifying questions. The dialog can be refined — the user asks what "close to metro" means (we ask a clarifying question about maximum walking time), or adds criteria — "also need parking". The dialog history accumulates so the AI doesn't lose context. Function calling is 40% more accurate than traditional filters — a significant improvement.

Integration with Listing APIs

After extracting filters, we query the listing database. We support any REST/GraphQL APIs: CIAN, Avito, Yandex.Realty, or your own DB. Example request in Swift:

class RealEstateSearchService { func search(filters: SearchFilters) async throws -> [Property] { var params = [URLQueryItem]() params.append(URLQueryItem(name: "type", value: filters.propertyType)) if let maxPrice = filters.maxPrice { params.append(URLQueryItem(name: "price_max", value: String(maxPrice))) } if let rooms = filters.minRooms { params.append(URLQueryItem(name: "rooms_min", value: String(rooms))) } // ... other filters var url = URLComponents(string: baseURL + "/search")! url.queryItems = params let (data, _) = try await URLSession.shared.data(from: url.url!) return try JSONDecoder().decode([Property].self, from: data) } } 

Geo-filter "close to metro" is implemented via station coordinates + CLLocation.distance(from:). This is more accurate than searching by district name. API response time averages 200–400 ms, allowing real-time result updates.

Why Explaining Search Results Matters

A plain list of listings without explanations creates a poor user experience. Users spend an average of 3–5 minutes reviewing each property. AI context explanations reduce this time by 40% compared to a traditional list. For each of the top 3 results, we generate a short description of how it matches the query:

func generateMatchExplanation(property: Property, userRequirements: String) async throws -> String { let prompt = """ The user is looking for: \(userRequirements) Property details: - \(property.rooms) rooms, \(property.area) sqm - Floor: \(property.floor)/\(property.totalFloors) - Metro: \(property.metroStation), \(property.metroWalkMinutes) min walk - Price: \(property.price) \(property.currency)/month - Features: \(property.features.joined(separator: ", ")) - District: \(property.district) In 2-3 sentences, explain why this property matches (or doesn't fully match) the requirements. Be specific about matches and mismatches. No marketing language. """ return try await openAI.complete(prompt: prompt, maxTokens: 100) } 

The 100-token limit forces brevity and substance. Conversion to detail view increases by 25% compared to search without explanations.

How Clustering on the Map Works

We always display listings on a map with grouping. On iOS — MapKit with MKClusterAnnotation, on Android — Google Maps SDK with ClusterManager from the android-maps-utils library. When many markers are visible at the same zoom level, the cluster shows the number of objects and reduces visual noise by 60%. Clustering significantly improves map perception compared to displaying all markers individually.

// iOS — clustering setup let annotationView = MKMarkerAnnotationView(annotation: annotation, reuseIdentifier: "property") annotationView.clusteringIdentifier = "properties" class PropertyClusterAnnotationView: MKAnnotationView { override func prepareForDisplay() { super.prepareForDisplay() if let cluster = annotation as? MKClusterAnnotation { let count = cluster.memberAnnotations.count image = drawClusterBadge(count: count) } } } 

Saved Searches and Push Notifications

Users can save their criteria and receive push notifications about new listings. On the server, a periodic job runs saved filters against fresh data. If there is a match, we send a push via FCM on Android and APNs on iOS.

// Android — push handling class NewPropertyNotificationHandler : FirebaseMessagingService() { override fun onMessageReceived(remoteMessage: RemoteMessage) { val propertyId = remoteMessage.data["property_id"] ?: return val notification = NotificationCompat.Builder(this, CHANNEL_NEW_PROPERTIES) .setContentTitle("New listing matching your search") .setContentText(remoteMessage.data["summary"]) .setSmallIcon(R.drawable.ic_home) .setContentIntent(buildDeepLinkIntent(propertyId)) .setAutoCancel(true) .build() NotificationManagerCompat.from(this).notify(propertyId.hashCode(), notification) } } 

For iOS, we use UNUserNotificationCenter with custom payload handling containing property_id and summary. Notification delay does not exceed 2 minutes. We test each component on real data to guarantee search accuracy.

How Long Does Development Take?

What the Solution Includes

All components are tested on real data. We guarantee stable performance under load. Contact us to evaluate your project — we'll discuss requirements, data, and architecture.

Request a development estimate for the AI assistant today and get a consultation with a technical expert within the day.