Predictive Showing Change Application for Real Estate Pricing
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Solution Overview
Problem
Real estate sellers often set home prices based on emotions and expectations, leading to inefficiencies in sales processes, as current methods lack effective tools for predicting the number of showings and optimizing pricing strategies.
Innovation Solution
A system and method using a handheld device with a predictive showings change application that compares data from a subject real estate listing to multiple comparable listings, allowing users to input current and suggested prices, and displays the results as interactive graphs to predict the number of showings based on average historical data from comparable properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sellers set prices based on emotions and expectations, then the pricing process is simple and quick, but the pricing accuracy and sales effectiveness deteriorate
Solution Approach 1:
The system implements feedback by comparing actual showing data from comparable properties against predicted showing numbers, allowing sellers to adjust pricing strategies based on measured outcomes. The feedback loop continues as new showing data is collected and integrated into future predictions, progressively improving pricing accuracy while maintaining computational efficiency.
Solution Approach 2:
The system performs preliminary action by calculating predicted showing numbers and providing pricing recommendations before the actual listing goes live. This allows sellers to optimize their pricing strategy in advance based on data-driven predictions rather than emotional decisions, improving pricing accuracy while keeping the process efficient through automated calculations.
2Measurement precision
If agents use data-driven pricing methods, then pricing accuracy improves, but the complexity of the pricing process increases
Solution Approach 1:
The system applies self-service by automatically collecting showing data from multiple sources, performing calculations, and generating pricing recommendations without requiring manual data entry or complex analysis by the agent. The automated system handles the complexity internally while presenting simple, actionable insights to the user, thus improving pricing accuracy without increasing perceived process complexity.
Solution Approach 2:
The system achieves universality by integrating multiple functions into a single platform: data collection from various sources, predictive analytics, comparable property analysis, and recommendation generation. This multi-functional approach consolidates what would otherwise be separate complex processes into one unified system, improving pricing accuracy while maintaining ease of use through a single interface.
3Productivity
If sellers optimize pricing strategies, then sales effectiveness improves, but the time required for analysis increases
Solution Approach 1:
The system replaces manual mechanical analysis with automated computational processing. Instead of agents manually analyzing showing data and making pricing decisions, the system automatically processes data, performs predictive analytics, and generates recommendations in seconds. This substitution dramatically reduces analysis time while improving sales effectiveness through data-driven optimization.
Solution Approach 2:
The system performs preliminary action by pre-calculating showing predictions and pricing recommendations before the listing is active. This advance preparation allows sellers to make informed pricing decisions quickly when the time is critical, improving sales effectiveness without requiring extensive analysis time during the listing process.
Data Source
AI summary
A method for setting or adjusting a sale price for a subject real estate listing including receiving data regarding a subject real estate listing from a handheld device operating a predictive showing change application. The data regarding the subject real estate listing is compared with a multiple of comparable real estate listings and an output that compares a number of showings for a suggested price of the subject real estate listing compared to a number of showings based on the multiple of comparable real estate listings is determined.