System for dynamic real estate valuation based on multiparametric market indicators

Abstract

A dynamic real estate valuation system based on multiparametric market indicators, which includes the following: a valuation engine configured to generate real-time results for property valuation; a multitude of distributed data ingestion and processing units configured to capture heterogeneous data sources, including historical property transaction data, real-time property listings, zoning and land use records, macroeconomic indicators, geospatial data, environmental sensor outputs, and sentiment-derived metrics; a model orchestration control unit comprising a stack of machine learning models, wherein the models include at least a gradient boosting decision tree model, a long-short-term memory (LSTM) time series forecaster, and an enhancement learning module that iteratively optimizes the model parameters based on the observed evaluation accuracy; a data contextualization controller configured to apply dynamic weighting to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) that includes an edge processing unit (EPU), geolocation circuitry, secure communication interfaces and a touch-based user interface; a valuation book subsystem configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger; wherein the system is designed to continuously recalibrate its valuation results by comparing the predicted valuations with the actual sales or rental prices, and wherein the physical terminal is designed to produce a legally certifiable valuation document with embedded provenance data.

Description

Field of invention

[0001] The present invention relates to the technical field of real estate data analysis, predictive modeling, and valuation systems. Specifically, it relates to a dynamic real estate valuation system that utilizes a multi-parametric analysis framework encompassing real-time and historical market indicators, geodata, and AI-based valuation techniques. The invention also relates to a specific device implemented as an intelligent valuation engine or networked structural node within a smart city infrastructure, enabling automated, context-aware real estate valuation in the residential, commercial, and mixed-use property segments. Background of the invention

[0002] Traditional real estate valuation methods have long relied on static valuation models, manual appraiser input, and comparable sales data, often resulting in outdated and inaccurate valuations. These models are unable to respond in real time to volatile market conditions, urban development trends, or economic indicators. As global real estate markets become increasingly dynamic and complex, conventional models fail to account for nonlinear variables such as interest rate fluctuations, socioeconomic indices in residential areas, zoning changes, and infrastructure developments.

[0003] While some technical valuation tools already exist, these are often limited to simplified regression models based on a narrow set of data parameters and fail to consider the gradual decline in data relevance or local urban dynamics. Furthermore, existing systems lack the capability for continuous self-updating based on federated and distributed datasets. They are also not integrated into the physical urban infrastructure to enable location-based, real-time delivery. Therefore, there is a need for a robust, automated system and device for dynamic real estate valuation that adapts to a multi-parameter real estate environment using AI, geospatial data, sensor input, and market sentiment analysis.

[0004] Property valuation has long been a cornerstone of real estate markets, forming the basis for crucial decisions in buying, selling, investing, taxation, insurance, mortgage lending, and urban planning. Traditionally, property valuation has relied heavily on manual appraisals by certified professionals who estimate a property's value using comparative market analysis (CMA). This method evaluates recently sold similar properties, taking into account differences in size, location, condition, and features. While this manual approach is widely used, it is time-consuming, prone to subjective bias, and does not adequately respond to real-time market fluctuations.The reliance on static data often means that valuations are already outdated by the time they are completed, particularly in rapidly changing urban environments where property values ​​can change significantly within a short period of time due to economic, infrastructural or demographic developments.

[0005] In an effort to automate and scale the valuation process, several automated valuation models (AVMs) have been developed over the past two decades. These AVMs use statistical models, regression analysis, and sometimes basic machine learning techniques to estimate the value of properties based on publicly available data such as historical sales, tax assessments, and property attributes. Commercial examples include Zillow's Zestimate, CoreLogic's Total Home ValueX, and Redfin's AVM models. While these platforms offer instant valuations, they often lack transparency regarding the model logic and exhibit large confidence intervals. Many AVMs suffer from high variance in their results, particularly in heterogeneous markets where comparable sales data is sparse or inconsistent.Furthermore, these models tend to treat real estate as isolated units and do not take into account changes at the neighborhood level, macroeconomic indicators, and user sentiment, which are increasingly relevant to the modern dynamics of the real estate industry.

[0006] A major limitation of current AVMs is their reliance on fixed input parameters that adapt poorly to temporal changes or geographical specifics. For example, a regression model trained on five years of historical transaction data may be unsuitable for capturing the impact of a newly opened subway station, a change in zoning regulations, or a recent environmental disaster with dramatic consequences for local attractiveness. The lack of integration with real-time data streams limits their usefulness for decision-making by financial institutions, urban planners, and policymakers.Another shortcoming is that many existing AVMs treat the valuation problem as a univariate regression task, focusing narrowly on the selling price, while neglecting multifactorial influences such as school district performance, digital connectivity assessments, environmental sustainability assessments, or demographic changes.

[0007] Efforts to improve valuation models through the integration of geographic information system (GIS) data have yielded some improvements. GIS-enabled platforms can now map properties to layers representing flood risk, proximity to amenities, transportation networks, or urban zoning data. However, the integration is often superficial, and these systems do not dynamically adapt to changes over time. A GIS layer representing a planned subway line or commercial complex may fail to capture the impact of actual construction or delays. The result is a model that predicts potential value increases too early or too late.Furthermore, such systems typically lack a feedback mechanism to learn from real-world results, such as the discrepancy between predicted and actual transaction prices or the delay between approval and actual construction.

[0008] More sophisticated solutions attempt to apply machine learning to real estate valuation, employing ensemble models and deep learning to improve predictive accuracy. While these methods show promise, they still face significant challenges. The quality and completeness of training data remains a critical issue. In many countries, real estate transaction data is either not publicly available or incomplete, necessitating extensive data cleansing and enrichment. Furthermore, although deep learning models can model complex, nonlinear relationships, they often behave like black boxes, raising concerns about interpretability and compliance in financial or legal applications.Without transparency, stakeholders such as lenders, investors, or regulators might hesitate to rely solely on machine-generated valuations, especially for high-value transactions.

[0009] Real-time data integration remains another unresolved challenge. Existing systems do not efficiently capture and process live feeds of market data such as interest rate changes, inflation trends, or building material cost indices—all factors that can influence property prices. Furthermore, sentiment data from social media, local news, and civic engagement platforms is often not included in valuations, even though there is growing evidence that such signals can predict changes in neighborhood attractiveness. Current valuation systems lack robust natural language processing (NLP) or sentiment analysis modules that could enable contextual awareness. For example, a sudden spike in complaints about crime or pollution on social media could be an early warning sign of impending depreciation that conventional systems completely miss.

[0010] Existing real estate valuation solutions lack a dynamic, multi-parametric, real-time, and physically deployable framework that adapts to changing market conditions. They lack the integration of environmental sensors, real-time economic indicators, NLP modules for sentiment analysis, blockchain anchoring for verifiability, and edge AI deployment at physical urban nodes. Therefore, there is a significant need for a holistic, intelligent, and decentralized system that enables dynamic real estate valuation based on comprehensive market indicators, geographic information, and stakeholder feedback—supported by a secure, transparent, and adaptive digital infrastructure. Summary of the invention

[0011] The invention describes a system and a corresponding physical device for dynamic real estate valuation. It captures a variety of real-time and historical parameters, synthesizes the data using advanced machine learning models, and continuously generates updated real estate valuations. The system comprises a cloud-based valuation engine, distributed physical valuation terminals (devices), a secure data lake infrastructure, and a federated learning model that ensures local sensitivity and global relevance.

[0012] The physical device – referred to here as a "Property Valuation Terminal" (PVT) – is designed as an edge AI structure with embedded sensors, geolocation interfaces, communication modules (5G / LoRaWAN), and modules for secure data exchange. These terminals can be deployed in real estate offices, municipal valuation stations, or at urban smart poles. The system supports user interaction, the printing of valuation certificates, and the blockchain anchoring of certified values ​​for legal and transactional use. Furthermore, the system enables the prediction of property prices based on simulated economic and infrastructural developments.

[0013] The main objective of the present invention is to provide a dynamic, intelligent, and adaptive system for real-time property valuation. It utilizes a wide range of market indicators, geodata, environmental data, and machine learning models to deliver precise, context-sensitive, and legally verifiable valuations. The invention aims to overcome the limitations of traditional valuation methods and existing automated valuation models by enabling a system that continuously adapts to real-world changes in economic, infrastructural, demographic, and environmental conditions.Another objective of the invention is to provide a physical device - the Property Valuation Terminal (PVT) - that can be deployed in various urban, suburban or institutional environments and offers on-demand valuation services with real-time data fusion, secure communication and optional blockchain-based certification.

[0014] A further objective of the invention is to improve valuation accuracy at both the macro and micro levels through the integration of temporal sensitivity modeling, predictive learning mechanisms, and feedback-based self-calibration techniques. The system is intended to enable valuations that are not only based on historical sales but are also dynamically influenced by current parameters such as interest rate changes, local sentiment, neighborhood development, traffic expansion, or environmental quality indicators. This allows stakeholders—including buyers, sellers, banks, insurance companies, municipal tax authorities, and urban planners—to make data-driven decisions with greater certainty and legal certainty.

[0015] Another objective is the integration of decentralized learning and federated model updates. This ensures that valuation models are trained locally on regional specifics while maintaining data privacy. The invention also aims to ensure the auditability and legal traceability of each valuation transaction by embedding cryptographic hash-chaining and decentralized ledger storage mechanisms. Furthermore, the invention aims to connect the digital and physical worlds by embedding valuation information directly into edge devices that are part of the smart city infrastructure. This makes dynamic property valuation available on demand and improves integration with public services, real estate transactions, and financial insurance.

[0016] Ultimately, the invention aims to democratize and decentralize the evaluation process by making it intelligent, real-time, environmentally conscious and infrastructurally embedded, and exhibiting a level of automation, transparency and reliability that was not achievable with existing approaches. BRIEF DESCRIPTION OF THE FIGURE

[0017] These and other features, aspects, and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols consistently represent the same parts. The following applies: Fig. Figure 1 shows a block diagram of a dynamic real estate valuation system based on market indicators with multiple parameters.

[0018] Experts will also recognize that the elements in the drawing are shown for the sake of simplicity and are not necessarily to scale. For example, the flowcharts illustrate the process by highlighting the main steps to enhance understanding of the aspects of this disclosure. Furthermore, with regard to the design of the device, one or more components of the device may be represented in the drawing by conventional symbols, and the drawing may show only the specific details relevant to understanding the embodiments of this disclosure, so as not to clutter the drawing with details that are readily apparent to those skilled in the art after reading this description. Detailed description of the invention

[0019] For a better understanding of the inventive principles, reference is made below to the embodiment shown in the drawing, which is described in specific terminology. However, this does not limit the scope of the invention. Changes and further modifications of the illustrated system, as well as further applications of the inventive principles, are possible, as would normally occur to a person skilled in the art in the field of invention.

[0020] It is clear to the person skilled in the art that the preceding general description and the following detailed description are exemplary and explanatory of the invention and are not intended as a limitation of it.

[0021] References in this specification to “an aspect”, “another aspect”, or similar expressions mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the expressions “in one embodiment”, “in another embodiment”, and similar expressions in this specification may all refer to the same embodiment, but need not.

[0022] The terms "includes," "include," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method that includes a list of steps may not only contain those steps but may also include other steps not expressly listed or inherent in such process or method. Likewise, the statement "includes..." in the case of one or more devices, subsystems, elements, structures, or components does not, without further limitations, preclude the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by a person skilled in the art in the field of the invention. The system, methods, and examples provided here serve only for illustration and are not to be construed as a limitation.

[0024] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0025] Fig.Figure 1 shows a block diagram of a dynamic real estate valuation system based on multiparametric market indicators. The system 100 comprises: a valuation engine (102) configured to generate real-time real estate valuation results; multiple distributed data ingestion processing units (104) configured to ingest heterogeneous data sources, including historical real estate transaction data, real-time real estate listings, zoning and land-use records, macroeconomic indicators, geospatial information, environmental sensor results, and sentiment-derived metrics; a model orchestration control unit (106) comprising a stack of machine learning models, the models including at least a gradient-boosting decision tree model, a long short-term memory (LSTM) time-series forecaster, and an amplification learning module that iteratively optimizes model parameters based on observed valuation accuracy;a data contextualization controller (108) configured to dynamically assign weights to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) (110) comprising an edge processing unit (EPU) (110a), geolocation circuitry, secure communication interfaces, and a touch-based user interface; a valuation ledger subsystem (112) configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger;wherein the system is adapted to continuously recalibrate its valuation outputs by comparing predicted valuations with actual sales or rental prices, and wherein the physical terminal can be operated to produce a legally certifiable valuation document with embedded provenance data.

[0026] In one embodiment, the data acquisition modules (104) comprise natural language processing pipelines configured to extract market sentiment, community perception, and regulatory changes from unstructured sources, including social media feeds, planning commission communications, and regional news articles, vectorizing these extracted signals using transformer-based language models and assigning them influence coefficients based on temporal relevance and geographical proximity to the property in question.

[0027] In one embodiment, the data contextualization controller (108) applies a temporal Gaussian decay kernel to each time-dependent parameter such that older market data are downweighted unless they are amplified by correlated signals in the current data set, and wherein the decay kernel is adaptively changed for each geographic cell based on volatility metrics for real estate transactions and temporal autocorrelation coefficients.

[0028] In one embodiment, the model orchestration control unit (106) is configured to use a meta-learner that dynamically selects and mixes outputs of the underlying machine learning models based on local model performance metrics, model drift diagnoses, and the statistical structure of the input feature space, thus producing a hybridized evaluation output with minimized error propagation.

[0029] In one embodiment, the terminal (110) for valuing physical property comprises a secure element and a biometric authentication module for verifying the identity of authorized users. The terminal also includes a localized cache of model weights and inference logic, enabling offline valuation with delayed synchronization with the central server and ledger upon reconnection.

[0030] In one embodiment, the valuation ledger subsystem (112) is implemented on a permission-based blockchain network that supports Byzantine fault tolerance and zero-knowledge proof protocols, and wherein each valuation entry includes a unique cryptographic hash of the input data vector, model identifier, valuation output and device ID, thereby enabling tamper-proof verifiability and chronological traceability of all valuation events.

[0031] In one embodiment, the edge processing unit (EPU) (110a) within the real estate valuation terminal comprises a tensor processing unit (TPU) or neural processing unit (NPU) capable of performing low-latency inferences for initial valuation, and wherein the unit is also configured to locally perform anomaly detection techniques to filter out input inconsistencies prior to cloud-level orchestration.

[0032] In one embodiment, the system is also configured to capture and simulate the effects of hypothetical future infrastructure developments – such as planned transport routes, rezoning plans or commercial complexes – using spatial-temporal modeling and predictive scenario analysis, thus enabling the estimation of the predicted real estate valuation trends under various planning conditions.

[0033] In one embodiment, the evaluation engine (102) includes an ensemble explanation module adapted to deconstruct the contribution of each input parameter using SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations) values, or integrated gradients, and to generate an interpretative justification for the final evaluation to ensure legal transparency and model accountability.

[0034] In one embodiment, the evaluation certificate generated by the terminal contains a dynamically rendered QR code linked to a secure cloud endpoint that provides a verification page displaying the evaluation timestamp, input data sources, model versioning metadata, and the cryptographic anchor in the blockchain ledger.

[0035] The scoring engine runs on dedicated computing hardware, such as a multi-core CPU- or GPU-accelerated server capable of executing complex scoring algorithms in real time. The distributed data acquisition processing units are implemented as network-connected edge devices or server-side appliances equipped with hardware interfaces and pre-configured storage and processing logic for ingesting, pre-processing, and transferring large volumes of heterogeneous datasets. The model orchestration control unit is implemented using dedicated machine learning accelerators or hardware-optimized containers deployed on a high-performance computing infrastructure, enabling the parallel execution of gradient boosting, LSTM, and reinforcement leaning models.The data contextualization controller uses embedded processors with matrix operation support and lookup table access to perform real-time parameter weighting based on geotemporal logic encoded in the firmware. The physical property valuation terminal (PVT) is a tangible hardware device that integrates an edge processing unit (EPU) and a human-machine interface based on a capacitive touchscreen. The valuation ledger subsystem includes cryptographic co-processors and secure storage modules for hashing, timestamping, and securely transferring valuation records to a blockchain ledger using tamper-proof hardware mechanisms. Each functional block of the system is therefore tied to specific physical hardware implementations that enable real-world operation, secure data processing, and legally certifiable results.

[0036] The dynamic real estate valuation system based on multiparametric market indicators utilizes a multi-layered architecture designed to capture, contextualize, and process a wide range of heterogeneous data sources through a tightly coupled set of technical modules. The technology begins by ingesting data from structured and unstructured channels. Structured data includes historical real estate transactions, tax assessments, cadastral records, and publicly available property attributes such as living area, number of rooms, lot size, age of construction, and energy performance certificates. Simultaneously, the unstructured data ingestion pipeline processes sentimental information from social media posts, local news articles, city council meeting minutes, planning commission documents, and real-time online listing descriptions using deep natural language processing (NLP) models.

[0037] After ingestion, the raw data is normalized and vectorized. For unstructured data, a transformer-based language model such as BERT or GPT-derived embeddings is used to generate context-rich vectors representing neighborhood sentiment, perceived safety, and regulatory changes. The structured data is processed by a temporal tagging module that appends timestamps, location hashes, and data confidence levels. The core of the technique then applies a contextualization framework that uses rule-based filters and machine-learned attention weights to dynamically modulate the influence of each data input. For example, a Gaussian decay kernel is applied to historical transaction prices, reducing their weight in the model as time elapses, unless recent inputs indicate confirmation of trends (e.g.,a revival of demand for a previously declining location due to new infrastructure developments).

[0038] The technique's valuation engine is orchestrated by a model ensemble that includes Gradient Boosting Decision Trees (GBDTs) for high-dimensional regression, Long Short-Term Memory (LSTM) networks for time-series forecasting, and reinforcement learning agents that continuously optimize valuation paths based on variance feedback. The reinforcement learning component is designed to minimize cumulative valuation errors across multiple property categories and locations by adjusting feature weights, training data selection heuristics, and model hyperparameters in real time. These agents receive a reward signal based on the observed accuracy of past valuations, particularly by comparing estimated values ​​to the final sale or rental price, taking into account macroeconomic variance and seasonal volatility.

[0039] The ensemble models are managed by a meta-leamer, a neural controller trained to predict the highest-performing model combination based on regional characteristics, data parity, and transaction density. For example, in a densely populated urban area, the meta-leamer might prioritize the output of LSTM models due to the availability of granular time-series data, while in rural or low-volume markets, it would default to Bayesian estimators to quantify uncertainty.

[0040] A key feature of the technique is its ability to perform assessments even with partial data availability. This is achieved through an imputation engine that uses K-nearest neighbors (KNN) for categorical and regression-based fill-ins for continuous variables, depending on spatial proximity and the clustering of feature archetypes. If full inference at the edge device is not possible, a lightweight approximation model pre-trained on local parameters can generate a temporary assessment, which is later corrected or validated once the complete feature sets are synchronized with the central server.

[0041] To ensure transparency and explainability, the system integrates post-hoc interpretability levels. When generating an assessment, the system uses SHAP (Shapley Additive Explanations) or LIME (Local Interpretable Model-Agnostic Explanations) techniques to calculate the marginal influence of each input variable on the final result. This allows the system to present, in addition to the assessment result, a ranking of influencing factors such as proximity to traffic, air quality index, current comparable sales figures, or sentiment polarity values.

[0042] Following inference, the valuation event is cryptographically secured. A hash of the input feature vector, model ID, device identifier, and valuation result is generated and written to an authorized blockchain ledger. The blockchain implementation supports zero-knowledge proof protocols and timestamped record chains, allowing downstream users (e.g., banks, regulators, legal professionals) to verify the valuation's origin without accessing confidential raw data. The output valuation is then formatted into jurisdiction-specific templates that conform to standards such as USPAP or IVS and optionally converted into a digitally signed certificate with an embedded QR code referencing the blockchain record and the summary of the model's explanatory capabilities.

[0043] For federated learning, the system divides its training corpus into regional nodes, with each edge evaluation device managing a localized subset of the training data. Model training takes place on the device, and only the resulting gradient updates are transmitted to a central aggregator via secure, differentially private channels. The central aggregator consolidates global updates without exposing sensitive object-level data and returns regularly updated weights to the devices. This creates a self-learning network that adapts across locations to evolving market dynamics without compromising data sovereignty.

[0044] The valuation method also includes a scenario simulation layer for predictive modeling. Upon receiving data on planned future developments—such as new highways, subway lines, industrial zones, or zoning changes—the method calls upon a spatial simulation engine that forecasts the long-term impact on nearby property values. Using Monte Carlo simulations embedded in the valuation model, it generates projection curves for multiple planning scenarios, taking into account uncertainty ranges, political acceptance rates, and historical analogies.

[0045] The technology also includes a feedback module that continuously logs the deviation between predicted valuation and actual transaction completion, adjusts model weights, and notifies system administrators when the cumulative forecast errors for a given micromarket exceed statistically determined control limits. This drift detection mechanism triggers targeted retraining and reoptimization of the hyperparameters, ensuring the system remains resilient to systemic shocks, policy changes, and behavioral shifts.

[0046] The system comprises a central, AI-driven valuation engine connected to a distributed network of edge computing valuation devices and a secure, dynamic market data repository. At its core, the central engine integrates data from various structured and unstructured sources, including public registry databases, building information models (BIM), real-time property listings, municipal planning updates, IoT sensor feeds, traffic and accessibility assessments, demographic profiles, school district ratings, crime indices, energy performance certificates, and property transaction histories.

[0047] The machine learning framework comprises several layers of neural networks, including gradient-boosting decision trees, LSTM models for time-series forecasting, and reinforcement learning modules that continuously optimize evaluation techniques based on feedback regarding evaluation deviations. A geospatial analysis engine, built on a dynamic GIS layer, maps neighborhood transformation metrics and overlays zoning, flood risk, infrastructure investments, and socioeconomic data.

[0048] The physical Property Valuation Terminal (PVT) device consists of the following components: 1. Embedded Processing Unit (EPU): Contains a TPU-enabled microprocessor that enables on-device inference for basic evaluation tasks and preprocessing for cloud delivery. 2. Display and interface module: A touch-sensitive interface for data input, QR code scanning of property identifiers, biometric authentication (optional) and real-time visualization of results. 3. Environmental sensor suite: Integrated sensors for capturing site-specific parameters such as noise levels, air quality, temperature and pedestrian frequency - parameters that can influence the attractiveness of a micro-location and thus its rating. 4. Secure communication stack: Includes modules for 5G, LTE, Wi-Fi and LoRaWAN for data transmission to and from central evaluation servers and decentralized municipal databases. 5. Data storage and security module: Equipped with encrypted flash memory and secure enclaves for temporarily caching confidential inputs before secure push to centralized or federated learning models. 6. Blockchain anchor module: For the hash chaining of each valuation request and output, thereby ensuring verifiability and legal admissibility for compliance with regulations in financial and legal applications. 7. Printer and certificate module: Enables the issuance of an evaluation certificate with a dynamic QR code linked to the source evaluation record, timestamp, and blockchain proof.

[0049] The dynamic evaluation process technique comprises the following phases: 1. Data fusion from multiple sources: Incorporates various real-time data, including interest rates from central bank APIs, live property listings, rental prices from syndicated brokers, building permits, and social media sentiment regarding safety and services in the neighborhood. 2. Contextualization level: The technique filters and weights data sources based on location- and property-type-specific rules. For example, proximity to a subway line can be weighted more heavily in an urban context than in suburban environments. 3. Modeling temporal sensitivity: Data inputs are decrease-weighted using Gaussian time functions, so that older transactions or policies have less influence unless amplified by current correlated events. 4. Valuation modeling and output generation: Using a stacked ensemble of models, including deep neural networks, Bayes estimators and time series regressors, the final valuation is generated with a confidence interval and a volatility projection. 5. Certification and Blockchain Anchoring: The valuation result is hashed and recorded in a decentralized ledger with cryptographic links to the input data, creating a secure record suitable for financial underwriting or legal filing. 6. Feedback loop and adaptive learning: The system collects downstream performance indicators - such as actual sales compared to the estimated valuation - and refines the model parameters through reinforcement signals.

[0050] The valuation device is optionally designed for operation as part of a smart pole infrastructure in modern cities. It receives municipal data via IoT mesh networks and responds to API calls from authorized stakeholders (banks, notaries, real estate agents, municipal tax authorities). In scenarios requiring offline operation, the system can utilize satellite communication fallback and edge-only AI models with delayed synchronization to the ledger.

[0051] The drawing and the preceding description show examples of embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another embodiment. For example, the sequence of the processes described here can be changed and is not limited to the manner described here. Furthermore, the actions of a flowchart need not be implemented in the sequence shown; nor does it necessarily have to be performed by all actions. Actions that are not dependent on other actions can also be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations are possible, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use. The range of embodiments is at least as broad as specified in the following claims.

[0052] Advantages, further benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and all components that can lead to an advantage, benefit, or solution occurring or becoming more apparent are not to be construed as critical, necessary, or essential features or components of individual or all claims. REFERENCES 100 A system for dynamic real estate valuation based on multiparametric market indicators. 102 rating machine 104 Distributed Data Acquisition and Processing Units 106 Model Orchestration Control Unit 108 Data Contextualization Controller 110 Terminal for the Valuation of Physical Real Estate 110a Edge processing unit 112 Assessment Ledger Subsystem

Claims

[1] A system for dynamic property valuation based on multiparametric market indicators, comprising the following: a valuation engine configured to generate real-time results for property valuation; a multitude of distributed data ingestion and processing units configured to capture heterogeneous data sources, including historical property transaction data, real-time property listings, zoning and land use records, macroeconomic indicators, geospatial data, environmental sensor outputs, and sentiment-derived metrics; a model orchestration control unit comprising a stack of machine learning models, wherein the models include at least a gradient boosting decision tree model, a long-short-term memory (LSTM) time series forecaster, and an enhancement learning module that iteratively optimizes the model parameters based on the observed evaluation accuracy; a data contextualization controller configured to apply dynamic weighting to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) that includes an edge processing unit (EPU), geolocation circuitry, secure communication interfaces and a touch-based user interface; a valuation book subsystem configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger; wherein the system is designed to continuously recalibrate its valuation results by comparing the predicted valuations with the actual sales or rental prices, and wherein the physical terminal is designed to produce a legally certifiable valuation document with embedded provenance data. [2] System according to claim 1, wherein the data acquisition modules comprise natural language processing pipelines configured to extract market sentiment, community perception and regulatory changes from unstructured sources, including social media feeds, planning commission communications and regional news articles, wherein these extracted signals are vectorized using transformer-based language models and influence coefficients are assigned to them based on temporal relevance and geographical proximity to the property in question. [3] System according to claim 1, wherein the data contextualization controller applies a temporal Gaussian decay kernel to each time-dependent parameter such that older market data are downweighted unless they are amplified by correlated signals in the current data set, and wherein the decay kernel is adaptively changed for each geographic cell based on volatility metrics for real estate transactions and temporal autocorrelation coefficients. [4] System according to claim 1, wherein the model orchestration control unit is configured to use a meta-learner that dynamically selects and blends outputs of the underlying machine learning models based on local model performance metrics, model drift diagnoses and the statistical structure of the input feature space, thereby producing a hybridized evaluation output with minimized error propagation. [5] System according to claim 1, wherein the terminal for valuing physical property comprises a secure element and a biometric authentication module for verifying the identity of authorized users, and wherein the terminal includes a localized cache with model weights and inference logic that enables an offline valuation function with delayed synchronization with the central server and ledger upon reconnection. [6] System according to claim 1, wherein the valuation ledger subsystem is implemented on a permissioned blockchain network supporting Byzantine fault tolerance and zero-knowledge proof protocols, and wherein each valuation entry includes a unique cryptographic hash of the input data vector, model identifier, valuation output and device ID, thereby enabling tamper-proof verifiability and chronological traceability of all valuation events. [7] System according to claim 1, wherein the edge processing unit (EPU) within the real estate valuation terminal comprises a tensor processing unit (TPU) or neural processing unit (NPU) capable of performing low-latency inferences for the initial valuation, and wherein the unit is further configured to perform anomaly detection techniques locally to filter out input inconsistencies prior to cloud-level orchestration. [8] System according to claim 1, wherein the system is further configured to capture and simulate the effects of hypothetical future infrastructure developments - such as proposed transport routes, rezoning plans or commercial complexes - using spatial-temporal modeling and predictive scenario analysis, thus enabling the estimation of the predicted development of property valuation under different planning conditions. [9] System according to claim 1, wherein the evaluation engine comprises an ensemble explainability module adapted to deconstruct the contribution of each input parameter using SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations) values ​​or integrated gradients and to generate an interpretative justification for the final evaluation to ensure legal transparency and model accountability. [10] System according to claim 1, wherein the evaluation certificate generated by the terminal contains a dynamically rendered QR code linked to a secure cloud endpoint that provides a verification page displaying the evaluation timestamp, input data sources, model versioning metadata and the cryptographic anchor in the blockchain ledger.

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