Disclaimer

About the Data, Models and Methods

The insights, indices, scores, and recommendations provided on this website are generated using a technology-enabled, data-driven and statistical modelling framework. These outputs are intended to assist users in understanding market trends and rationalising the home selection and evaluation process. They should not be construed as financial, legal, or investment advice.

Data Sources
  • Data is collected from a combination of primary surveys and publicly available sources.
  • Rental estimates are derived from prevailing rentals of nearby properties, using publicly available data sources such as IGR-registered rental agreements and other market references.
Data Processing and Limitations
  • Real estate data is large-scale, sourced from multiple origins, and is often unstructured, inconsistent, and noisy.
  • To address this, the data undergoes multiple stages of processing, including standardisation, cleaning, outlier removal, and geocoding.
  • Machine learning techniques and statistical methods are used to structure and refine the data.
  • Geocoding is carried out using third-party services (such as Google APIs), which may occasionally result in inaccuracies.

Despite rigorous processing, data discrepancies may still exist, which can impact model outputs.

Model Assumptions and Market Behaviour
  • Certain market phases or time periods may deviate from established economic or real estate theories.
  • In such instances, model outputs may appear inconsistent with short-term market behaviour.
  • While the models have been tested and found reliable in most scenarios, particularly over the long term, no model can fully capture all market dynamics at all times.
Use of Regression and Data Augmentation
  • Transaction, supply, and rental data are often limited at any given point in time and may not fully represent the entire city or regional market.
  • Advanced regression and statistical models are therefore used to augment and populate datasets, including price estimation, household density, rental values, and land price indicators.
Potential Reasons for Inaccurate Predictions
  1. 1. Geocoding Errors:

    Property locations are geocoded using third-party mapping APIs. As real estate valuation is highly location-sensitive, any error in geocoding may lead to incorrect outputs

  2. 2. Infrastructure Impact Assumptions:
    • Upcoming infrastructure projects are mapped using GIS tools, and travel-time improvements are estimated accordingly.
    • These estimates do not fully account for future traffic patterns, congestion, or behavioural changes, which may affect actual outcomes.
  3. 3. Rental Yield Estimation:

    Rental yield calculations are based on average rentals within the surrounding micro-market and do not factor in project-specific quality attributes, amenities, or management standards.

Developer and Project Ratings
  • Developer and project ratings are derived from a structured grading model based on the performance of ongoing projects.
  • The model assigns weighted scores to defined performance parameters.
  • Data is sourced through quarterly primary surveys and public information.
  • Ratings are dynamic in nature and may change as new or updated information becomes available.
Liability Disclaimer

This platform is a technology- and statistics-driven decision-support tool. By using this website, the user acknowledges and accepts the inherent limitations of data, models, and assumptions used.

Liases Foras, along with its promoters, directors, employees, and associates, does not assume any financial, legal, or other liability for errors, omissions, or inaccuracies in recommendations or outputs, whether arising from data discrepancies, modelling assumptions, third-party services, or any human, system, or automated (bot) error.

Users are advised to independently verify information and seek professional advice before making any financial or investment decisions.


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